Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29  ×  10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

Histopathology image classification: highlighting the gap between manual analysis and AI automation

The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training. The procedure of the detection can be automated to the reduction of the risks by identifying and flagging the suspicious frames. A suspicious frame may have some of the abnormality or the information about anatomical landmark in the frame. The frame then can be analysed for the anatomical landmarks and the abnormalities for the detection of disease. In this research, a real-time endoscopic abnormalities detection system is presented that detects the abnormalities and the landmarks. The proposed system is based on a combination of handcrafted and deep features. Deep features are extracted from lightweight MobileNet convolutional neural network (CNN) architecture. There are some of the classes with a small inter-class difference and a higher intra-class differences, for such classes the same detection threshold is unable to distinguish. The threshold of such classes is learned from the training data using genetic algorithm. The system is evaluated on various benchmark datasets and resulted in an accuracy of 0.99 with the F1-score of 0.91 and Matthews correlation coefficient (MCC) of 0.91 on Kvasir datasets and F1-score of 0.93 on the dataset of DowPK. The system detects abnormalities in real-time with the detection speed of 41 frames per second.

Machine learning in computational histopathology: Challenges and opportunities

Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.

Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

Deep learning in digital pathology image analysis: a survey

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.

Artificial intelligence in gastrointestinal endoscopy: The future is almost here

Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks

Deep learning using convolutional neural networks (CNNs) is a distinguished tool for many image classification tasks. Due to its outstanding robustness and generalization, it is also expected to play a key role to facilitate advanced computer-aided diagnosis (CAD) for pathology images. However, the shortage of well-annotated pathology image data for training deep neural networks has become a major issue at present because of the high-cost annotation upon pathologist's professional observation. Faced with this problem, transfer learning techniques are generally used to reinforcing the capacity of deep neural networks. In order to further boost the performance of the state-of-the-art deep neural networks and alleviate insufficiency of well-annotated data, this paper presents a novel stepwise fine-tuning-based deep learning scheme for gastric pathology image classification and establishes a new type of target-correlative intermediate datasets. Our proposed scheme is deemed capable of making the deep neural network imitating the pathologist's perception manner and of acquiring pathology-related knowledge in advance, but with very limited extra cost in data annotation. The experiments are conducted with both well-annotated gastric pathology data and the proposed target-correlative intermediate data on several state-of-the-art deep neural networks. The results congruously demonstrate the feasibility and superiority of our proposed scheme for boosting the classification performance.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

Purpose

Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention.

Methods

This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively.

Results

12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively.

Conclusion

Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PURPOSE

This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.

MATERIALS AND METHODS

In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.

RESULTS

Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%.

CONCLUSIONS

These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.

Novel structural descriptors for automated colon cancer detection and grading

The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer. However, the process is subjective and leads to significant inter/intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists. Therefore, a reliable computer-aided technique, which can automatically classify normal and malignant colon samples, and determine grades of malignant samples, is required. In this paper, we propose a novel colon cancer diagnostic (CCD) system, which initially classifies colon biopsy images into normal and malignant classes, and then automatically determines the grades of colon cancer for malignant images. To this end, various novel structural descriptors, which mathematically model and quantify the variation among the structure of normal colon tissues and malignant tissues of various cancer grades, have been employed. Radial basis function (RBF) kernel of support vector machines (SVM) has been employed as classifier in order to classify/grade colon samples based on these descriptors. The proposed system has been tested on 92 malignant and 82 normal colon biopsy images. The classification performance has been measured in terms of various performance measures, and quite promising performance has been observed. Compared with previous techniques, the proposed system has demonstrated better cancer detection (classification accuracy=95.40%) and grading (classification accuracy=93.47%) capability. Therefore, the proposed CCD system can provide a reliable second opinion to the histopathologists.

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space.

Two-tier tissue decomposition for histopathological image representation and classification

In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.

Ensemble classification of colon biopsy images based on information rich hybrid features

In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the performance of CBIC on original and reduced feature sets.

Multi-label classification for colon cancer using histopathological images

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.

A recent survey on colon cancer detection techniques

Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.

Computer aided measurement of sub-epithelial collagen band in colon biopsies for collagenous colitis diagnosis

This paper presents a novel computer aided technique for screening of Collagenous Colitis (CC). CC is a type of microscopic colitis mostly characterized by chronic watery diarrhea which is a common feature with a range of other etiologies. Routine paraclinical tests from CC patients such as endoscopic and radiographic studies are usually normal, and diagnosis must be made by biopsy. The gold standard for a confirmative diagnosis of CC is to measure the thickness of the sub-epithelial collagen (SEC) in colon tissue samples. Visual inspection of microscopic samples is often time-consuming, cumbersome and subject to human errors. This fact demonstrates the necessity of developing an automated method which assists pathologists in evaluating histopathological samples more accurately in the busy clinical environment. To the best of our knowledge, this is the first time that a computer-assisted diagnosis algorithm has been applied to CC detection. The proposed method uses a pre-trained Multi-Layer Perceptron neural network to segment SEC band in colon tissue images. We compared a variety of different color and texture descriptors and explore the best set of features for this task. The investigation of the proposed method shows 94.5% specificity and 95.6% sensitivity rate.

Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features

Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best classification performance.

AUTOMATIC DETECTION OF COLORECTAL POLYPS IN STATIC IMAGES

Colorectal cancer continues to be one of the leading causes of mortality worldwide. Scanning using colorectal endoscopy is a useful and common method in clinical examinations. However, the scanning and polyps detections are performed by physicians. Failures to detect polyps might be caused due to lack of experience or knowledge. The purpose of this paper is to discover a scheme able to distinguish polyps from normal tissue in static images off-line.Texture features are studied for the discrimination between polyps and normal tissue. Two useful and simple features are proposed. The student's t-test is applied in selecting useful features to reduce the computation time. The support vector machine is used as a classifier to identify the position of polyps. A study on the numbers in the training patterns is done in order to select an optimal ratio between the polyps and non-polyps sub-images. Seventy-four colonoscopic images are collected to test the system. Half are used as training images and half for testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain a single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 marks per image.

Coupled analysis of in vitro and histology tissue samples to quantify structure-function relationship

The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models.

A resampling-based Markovian model for automated colon cancer diagnosis

In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.

An expert support system for breast cancer diagnosis using color wavelet features

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Automated classification of renal cell carcinoma subtypes using bag-of-features

Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale invariant features for classification of renal cell carcinoma subtypes. Previous work shows that features from each subtype match those from expertly chosen template images. In this paper, we show that the performance of this match-based methodology greatly depends on the quality of the template images. To avoid this uncertainty, we propose a bag-of-features approach that does not require expert knowledge and instead learns a "vocabulary" of morphological characteristics from training data. We build a support vector machine using feature histograms and evaluate this method using 40 iterations of 3-fold cross validation. We achieve classification accuracy above 90% for a heterogeneous dataset labeled by an expert pathologist, showing its potential for future clinical applications.

Graph run-length matrices for histopathological image segmentation

The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.

Integrated diagnostics: a conceptual framework with examples

With the advent of digital pathology, imaging scientists have begun to develop computerized image analysis algorithms for making diagnostic (disease presence), prognostic (outcome prediction), and theragnostic (choice of therapy) predictions from high resolution images of digitized histopathology. One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer vision and image processing algorithms. Over the last decade, manifold learning and non-linear dimensionality reduction schemes have emerged as popular and powerful machine learning tools for pattern recognition problems. However, these techniques have thus far been applied primarily to classification and analysis of computer vision problems (e.g., face detection). In this paper, we discuss recent work by a few groups in the application of manifold learning methods to problems in computer aided diagnosis, prognosis, and theragnosis of digitized histopathology. In addition, we discuss some exciting recent developments in the application of these methods for multi-modal data fusion and classification; specifically the building of meta-classifiers by fusion of histological image and proteomic signatures for prostate cancer outcome prediction.

Computer-aided classification of zoom-endoscopical images using Fourier filters

This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two- and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.

Automated classification of renal cell carcinoma subtypes using scale invariant feature transform

The task of analyzing tissue biopsies performed by a pathologist is challenging and time consuming. It suffers from intra- and inter-user variability. Computer assisted diagnosis (CAD) helps to reduce such variations and speed up the diagnostic process. In this paper, we propose an automatic computer assisted diagnostic system for renal cell carcinoma subtype classification using scale invariant features. We capture the morphological distinctness of various subtypes and we have used them to classify a heterogeneous data set of renal cell carcinoma biopsy images. Our technique does not require color segmentation and minimizes human intervention. We circumvent user subjectivity using automated analysis and cater for intra-class heterogeneities using multiple class templates. We achieve a classification accuracy of 83% using a Bayesian classifier.

Wireless capsule endoscopy and endoscopic imaging: a survey on various methodologies presented

Wireless capsule endoscopy (WCE) is a recently established methodology that offers to medical doctors (gastroenterologists) the capability to examine the interior of the small intestine with a noninvasive procedure. Before the introduction of WCE, it was impossible for a physician to examine tissues of the small intestine without performing a surgical operation. Although WCE has the advantage of investigating the whole digestive system, the viewing and evaluation of each WCE video is a time-consuming process (2-3 h) for MD gastroenterologists. This makes the WCE methodology not widely efficient and acceptable by MDs.

Effect of AEE788 and/or Celecoxib on colon cancer cell morphology using advanced microscopic techniques

Analysis of changes in cancer cell morphology and cytoskeletal element induced by external stimuli is focus of current cancer chemotherapeutic studies. Cancer cell cytoskeleton is complex network of interwoven protein fibers composed of microtubules, microfilaments and intermediate filaments. These interwoven protein fibers are responsible for maintaining cell morphology, movement, adhesion and transmembrane signal transmission. In this study, morphological and cytoskeletal changes induced by AEE788 and/or Celecoxib on colon cancer cell HCT 15 were analyzed using advanced microscopic techniques. Cell proliferation assay was used for determining IC(50) of AEE788 and/or Celecoxib on HCT 15. Confocal microscopic analysis of AEE788 and/or Celecoxib treated HCT 15 was performed using Rhodamine-Phalloidin (actin stain) and Hoechst 33342 (nuclear stain). Atomic force (AFM) and scanning electron microscopic (SEM) studies were also performed to analyze cell morphology and cell wall extension (filopodia and lamellipodia). In addition, quantitative analysis of morphological parameters was studied using cellular image processing technique. This is the first report that combination of AEE788 and Celecoxib additively increase growth inhibition and cell death on human colon cancer cell HCT 15 as estimated by cell proliferation assay. Morphological analysis of AEE788 or Celecoxib treated HCT 15 cell for 24h have not revealed significant change in morphology under phase contrast microscopy. But, slight morphological changes were observed in combination (AEE788+Celecoxib) treated HCT 15 for 24h. In contrast, high resolution confocal laser fluorescence and atomic force microscopic studies have revealed cell shrinkage, disorganized actin filament and, loss of filopodia and lamellipodia. These changes were more prominent in combination of AEE788 and Celecoxib treated HCT 15 than either drug alone. These results may suggest antiproliferative and antimetastatic activity of AEE788 and/or Celecoxib. Quantitative analysis of morphological parameters using cellular image processing technique have shown decrease in mean area, perimeter, compactness and eccentricity of combination drug treated cells than either drug alone. These results further support the confocal and AFM study. Scanning electron microscopic study of AEE788 and/or Celecoxib treated HCT 15 has also shown morphological changes and loss of filopodia and lamellipodia. In conclusion, this investigation of morphological and cytoskeletal changes using advanced microscopic techniques present a significant foundation for evaluating anticancer activity of a drug and form a new strategy for evaluating effect of AEE788 and/or Celecoxib on colon cancer.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

ECM-Aware Cell-Graph Mining for Bone Tissue Modeling and Classification

Pathological examination of a biopsy is the most reliable and widely used technique to diagnose bone cancer. However, it suffers from both inter- and intra- observer subjectivity. Techniques for automated tissue modeling and classification can reduce this subjectivity and increases the accuracy of bone cancer diagnosis. This paper presents a graph theoretical method, called extracellular matrix (ECM)-aware cell-graph mining, that combines the ECM formation with the distribution of cells in hematoxylin and eosin (H&E) stained histopathological images of bone tissues samples. This method can identify different types of cells that coexist in the same tissue as a result of its functional state. Thus, it models the structure-function relationships more precisely and classifies bone tissue samples accurately for cancer diagnosis. The tissue images are segmented, using the eigenvalues of the Hessian matrix, to compute spatial coordinates of cell nuclei as the nodes of corresponding cell-graph. Upon segmentation a color code is assigned to each node based on the composition of its surrounding ECM. An edge is hypothesized (and established) between a pair of nodes if the corresponding cell membranes are in physical contact and if they share the same color. Hence, multiple colored-cell-graphs coexist in a tissue each modeling a different cell-type organization. Both topological and spectral features of ECM-aware cell-graphs are computed to quantify the structural properties of tissue samples and classify their different functional states as healthy, fractured, or cancerous using support vector machines. Classification accuracy comparison to related work shows that ECM-aware cell-graph approach yields 90.0% whereas Delaunay triangulation and simple cell-graph approach achieves 75.0% and 81.1% accuracy, respectively.

Color graphs for automated cancer diagnosis and grading

This paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify a tissue considering the spatial distributions of its cell nuclei, the proposed method relies on the use of distributions of multiple tissue components for the representation. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their endpoints. Subsequently, it extracts a new set of structural features from these color graphs and uses these features in the classification of tissues. Working with the images of colon tissues, our experiments demonstrate that the color-graph approach leads to 82.65% test accuracy and that it significantly improves the performance of its counterparts.

Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation

Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.

Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features

We present a new image quantification and classification method for improved pathological diagnosis of human renal cell carcinoma. This method combines different feature extraction methodologies, and is designed to provide consistent clinical results even in the presence of tissue structural heterogeneities and data acquisition variations. The methodologies used for feature extraction include image morphological analysis, wavelet analysis and texture analysis, which are combined to develop a robust classification system based on a simple Bayesian classifier. We have achieved classification accuracies of about 90% with this heterogeneous dataset. The misclassified images are significantly different from the rest of images in their class and therefore cannot be attributed to weakness in the classification system.

Improving Renal Cell Carcinoma Classification by Automatic Region of Interest Selection

In this paper, we present an improved automated system for classification of pathological image data of renal cell carcinoma. The task of analyzing tissue biopsies, generally performed manually by expert pathologists, is extremely challenging due to the variability in the tissue morphology, the preparation of tissue specimen, and the image acquisition process. Due to the complexity of this task and heterogeneity of patient tissue, this process suffers from inter-observer and intra-observer variability. In continuation of our previous work, which proposed a knowledge-based automated system, we observe that real life clinical biopsy images which contain necrotic regions and glands significantly degrade the classification process. Following the pathologist's technique of focusing on selected region of interest (ROI), we propose a simple ROI selection process which automatically rejects the glands and necrotic regions thereby improving the classification accuracy. We were able to improve the classification accuracy from 90% to 95% on a significantly heterogeneous image data set using our technique.

Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography

Colonic crypt morphological patterns have shown a close correlation with histopathological diagnosis. Imaging technologies such as high-magnification chromoendoscopy and endoscopic optical coherence tomography (OCT) are capable of visualizing crypt morphology in vivo. We have imaged colonic tissue in vitro to simulate high-magnification chromoendoscopy and endoscopic OCT and demonstrate quantification of morphological features of colonic crypts using automated image analysis. 2-D microscopic images with methylene blue staining and correlated 3-D OCT volumes were segmented using marker-based watershed segmentation. 2-D and 3-D crypt morphological features were quantified. The accuracy of segmentation was validated, and measured features are in agreement with known crypt morphology. This work can enable studies to determine the clinical utility of high-magnification chromoendoscopy and endoscopic OCT, as well as studies to evaluate crypt morphology as a biomarker for colonic disease progression.

Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchical classification approach

In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using a hierarchical classifier within a multi-scale framework. Pyramidal decomposition is used to reduce an image into its constituent scales. The cascaded image analysis across multiple scales is similar to the manner in which pathologists analyze histopathology. Nearly 600 different image texture features at multiple orientations are extracted at every pixel at each image scale. At each image scale the classifier only analyzes those image pixels that have been determined to be tumor at the preceding lower scale. Results of quantitative evaluation on 20 patient studies indicate (1) an overall accuracy of over 90% and (2) an approximate 8-fold savings in terms of computational time. Both the AdaBoost and Decision Tree classifiers were considered and in both cases tumor detection sensitivity was found to be relatively constant across different scales. Detection specificity was however found to increase at higher scales reflecting the availability of additional discriminatory information.

Computer-aided identification of ovarian cancer in confocal microendoscope images

The confocal microendoscope is an instrument for imaging the surface of the human ovary. Images taken with this instrument from normal and diseased tissue show significant differences in cellular distribution. A real-time computer-aided system to facilitate the identification of ovarian cancer is introduced. The cellular-level structure present in ex vivo confocal microendoscope images is modeled as texture. Features are extracted based on first-order statistics, spatial gray-level-dependence matrices, and spatial-frequency content. Selection of the features is performed using stepwise discriminant analysis, forward sequential search, a nonparametric method, principal component analysis, and a heuristic technique that combines the results of these other methods. The selected features are used for classification, and the performance of various machine classifiers is compared by analyzing areas under their receiver operating characteristic curves. The machine classifiers studied included linear discriminant analysis, quadratic discriminant analysis, and the k-nearest-neighbor algorithm. The results suggest it is possible to automatically identify pathology based on texture features extracted from confocal microendoscope images and that the machine performance is superior to that of a human observer.