1
|
Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer. Healthcare (Basel) 2022; 10:healthcare10050801. [PMID: 35627938 PMCID: PMC9142115 DOI: 10.3390/healthcare10050801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 02/01/2023] Open
Abstract
Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.
Collapse
|
2
|
Almalki YE, Soomro TA, Irfan M, Alduraibi SK, Ali A. Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer. SENSORS 2022; 22:s22051868. [PMID: 35271015 PMCID: PMC8915058 DOI: 10.3390/s22051868] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 12/25/2022]
Abstract
Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.
Collapse
Affiliation(s)
- Yassir Edrees Almalki
- Department of Medicine, Division of Radiology, Medical College, Najran University, Najran 61441, Saudi Arabia
- Correspondence:
| | - Toufique Ahmed Soomro
- Department of Electronic Engineering, Larkana Campus, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67450, Pakistan;
| | - Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia;
| | | | - Ahmed Ali
- Eletrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan;
| |
Collapse
|
3
|
Mohanty F, Rup S, Dash B, Majhi B, Swamy M. An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106266] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
4
|
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7695207. [PMID: 32462017 PMCID: PMC7238352 DOI: 10.1155/2020/7695207] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 03/19/2020] [Accepted: 04/02/2020] [Indexed: 11/17/2022]
Abstract
Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.
Collapse
|
5
|
Dias Pedro RW, Machado‐Lima A, Nunes FLS. Towards an approach using grammars for automatic classification of masses in mammograms. Comput Intell 2020. [DOI: 10.1111/coin.12320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | - Ariane Machado‐Lima
- Information Systems, School of Arts, Sciences and Humanities University of São Paulo São Paulo Brazil
| | - Fátima L. S. Nunes
- Electrical Engineering, Polytechnic School University of São Paulo São Paulo Brazil
- Information Systems, School of Arts, Sciences and Humanities University of São Paulo São Paulo Brazil
| |
Collapse
|
6
|
New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence. AJR Am J Roentgenol 2019; 212:300-307. [PMID: 30667309 DOI: 10.2214/ajr.18.20392] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.
Collapse
|
7
|
Zhao Y, Zhang J, Xie H, Zhang S, Gu L. Minimization of annotation work: diagnosis of mammographic masses via active learning. Phys Med Biol 2018; 63:115003. [PMID: 29697059 DOI: 10.1088/1361-6560/aac042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.
Collapse
Affiliation(s)
- Yu Zhao
- Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China. Author contributed to this work
| | | | | | | | | |
Collapse
|
8
|
Aerts HJWL. Data Science in Radiology: A Path Forward. Clin Cancer Res 2018; 24:532-534. [PMID: 29097379 PMCID: PMC5810958 DOI: 10.1158/1078-0432.ccr-17-2804] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 10/20/2017] [Accepted: 10/31/2017] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues. This information can be used throughout the clinical care path to improve diagnosis and treatment planning, as well as assess treatment response. This tremendous potential for clinical translation has led to a vast increase in the number of research studies being conducted in the field, a number that is expected to rise sharply in the future. Many studies have reported robust and meaningful findings; however, a growing number also suffer from flawed experimental or analytic designs. Such errors could not only result in invalid discoveries, but also may lead others to perpetuate similar flaws in their own work. This perspective article aims to increase awareness of the issue, identify potential reasons why this is happening, and provide a path forward. Clin Cancer Res; 24(3); 532-4. ©2017 AACR.
Collapse
Affiliation(s)
- Hugo J W L Aerts
- Harvard Medical School, Dana-Farber Cancer Institute, and Brigham and Women's Hospital, Boston, Massachusetts.
| |
Collapse
|
9
|
Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography. Med Phys 2017; 44:3726-3738. [DOI: 10.1002/mp.12316] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 04/11/2017] [Accepted: 04/26/2017] [Indexed: 11/07/2022] Open
|
10
|
Pérez NP, Guevara López MA, Silva A, Ramos I. Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography. Artif Intell Med 2014; 63:19-31. [PMID: 25555756 DOI: 10.1016/j.artmed.2014.12.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 11/21/2014] [Accepted: 12/04/2014] [Indexed: 12/18/2022]
Abstract
OBJECTIVE This work addresses the theoretical description and experimental evaluation of a new feature selection method (named uFilter). The uFilter improves the Mann-Whitney U-test for reducing dimensionality and ranking features in binary classification problems. Also, it presented a practical uFilter application on breast cancer computer-aided diagnosis (CADx). MATERIALS AND METHODS A total of 720 datasets (ranked subsets of features) were formed by the application of the chi-square (CHI2) discretization, information-gain (IG), one-rule (1Rule), Relief, uFilter and its theoretical basis method (named U-test). Each produced dataset was used for training feed-forward backpropagation neural network, support vector machine, linear discriminant analysis and naive Bayes machine learning algorithms to produce classification scores for further statistical comparisons. RESULTS A head-to-head comparison based on the mean of area under receiver operating characteristics curve scores against the U-test method showed that the uFilter method significantly outperformed the U-test method for almost all classification schemes (p<0.05); it was superior in 50%; tied in a 37.5% and lost in a 12.5% of the 24 comparative scenarios. Also, the performance of the uFilter method, when compared with CHI2 discretization, IG, 1Rule and Relief methods, was superior or at least statistically similar on the explored datasets while requiring less number of features. CONCLUSIONS The experimental results indicated that uFilter method statistically outperformed the U-test method and it demonstrated similar, but not superior, performance than traditional feature selection methods (CHI2 discretization, IG, 1Rule and Relief). The uFilter method revealed competitive and appealing cost-effectiveness results on selecting relevant features, as a support tool for breast cancer CADx methods especially in unbalanced datasets contexts. Finally, the redundancy analysis as a complementary step to the uFilter method provided us an effective way for finding optimal subsets of features without decreasing the classification performances.
Collapse
Affiliation(s)
- Noel Pérez Pérez
- Institute of Mechanical Engineering and Industrial Management (INEGI), Campus da FEUP, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal.
| | - Miguel A Guevara López
- Institute of Electronics and Telematics Engineering of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal; Institute of Mechanical Engineering and Industrial Management (INEGI), Campus da FEUP, Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal
| | - Augusto Silva
- Institute of Electronics and Telematics Engineering of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Isabel Ramos
- Faculty of Medicine - Centro Hospitalar São Joao, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
| |
Collapse
|
11
|
Miranda GHB, Felipe JC. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 2014; 64:334-46. [PMID: 25453323 DOI: 10.1016/j.compbiomed.2014.10.006] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Revised: 09/20/2014] [Accepted: 10/01/2014] [Indexed: 11/29/2022]
Abstract
BACKGROUND Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions. The user provides parameters such as contour, shape and density and the system gives a suggestion about the BI-RADS classification. METHODS Initially, values of malignancy were defined for each image descriptor, according to the BI-RADS standard. When analyzing contour, for example, our method considers the matching of features and linguistic variables. Next, we created the fuzzy inference system. The generation of membership functions was carried out by the Fuzzy Omega algorithm, which is based on the statistical analysis of the dataset. This algorithm maps the distribution of different classes in a set. RESULTS Images were analyzed by a group of physicians and the resulting evaluations were submitted to the Fuzzy Omega algorithm. The results were compared, achieving an accuracy of 76.67% for nodules and 83.34% for calcifications. CONCLUSIONS The fit of definitions and linguistic rules to numerical models provided by our method can lead to a tighter connection between the specialist and the computer system, yielding more effective and reliable results.
Collapse
Affiliation(s)
- Gisele Helena Barboni Miranda
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo at Ribeirão Preto, Avenida Bandeirantes, 3900, Ribeirão Preto 14040-901, SP, Brazil.
| | - Joaquim Cezar Felipe
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo at Ribeirão Preto, Avenida Bandeirantes, 3900, Ribeirão Preto 14040-901, SP, Brazil.
| |
Collapse
|
12
|
Pereira DC, Ramos RP, do Nascimento MZ. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:88-101. [PMID: 24513228 DOI: 10.1016/j.cmpb.2014.01.014] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Revised: 01/09/2014] [Accepted: 01/14/2014] [Indexed: 06/03/2023]
Abstract
In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.
Collapse
Affiliation(s)
- Danilo Cesar Pereira
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Rua Santa Adélia, 166, 09210170 Santo André, SP, Brazil.
| | - Rodrigo Pereira Ramos
- Colegiado de Engenharia Elétrica, Universidade Federal do Vale do São Francisco, Avenida Antônio Carlos Magalhães, 510, 48902300 Juazeiro, BA, Brazil.
| | - Marcelo Zanchetta do Nascimento
- Faculty of Computation (FACOM), Federal University of Uberlândia (UFU), Av. João Naves de Ávila, 2121, 38400902 Uberlândia, MG, Brazil.
| |
Collapse
|
13
|
Tan M, Pu J, Zheng B. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. Int J Comput Assist Radiol Surg 2014; 9:1005-20. [PMID: 24664267 DOI: 10.1007/s11548-014-0992-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 03/06/2014] [Indexed: 12/13/2022]
Abstract
PURPOSE Improving radiologists' performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. METHODS We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest, we performed the study using a tenfold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. RESULTS The area under the receiver operating characteristic curve [Formula: see text] was obtained for the classification task. The results also showed that the most frequently selected features by the SFFS-based algorithm in tenfold iterations were those related to mass shape, isodensity, and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. CONCLUSION In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a "second reader" in future clinical practice.
Collapse
Affiliation(s)
- Maxine Tan
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| |
Collapse
|
14
|
Sharaf-El-Deen DA, Moawad IF, Khalifa ME. A new hybrid case-based reasoning approach for medical diagnosis systems. J Med Syst 2014; 38:9. [PMID: 24469683 DOI: 10.1007/s10916-014-0009-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Accepted: 01/03/2014] [Indexed: 10/25/2022]
Abstract
Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.
Collapse
Affiliation(s)
- Dina A Sharaf-El-Deen
- Faculty of Computer and Information Sciences, Ain Shams University, Abbasia, Cairo, Egypt,
| | | | | |
Collapse
|
15
|
Jing H, Yang Y, Nishikawa RM. Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys 2012; 39:676-85. [PMID: 22320777 DOI: 10.1118/1.3675600] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CADx). The conventional approach in CADx is to first train a pattern-classifier based on a set of existing training samples and then apply this classifier to subsequent new cases. The purpose of this work is to improve the classification accuracy of a CADx classifier by making use of a set of known cases retrieved from a reference library that are similar to the case under consideration. METHODS In the proposed approach, the authors will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. The basic idea is to put more emphasis on those cases that are similar to the query. The proposed approach is demonstrated first using a linear classifier and then extended to a nonlinear classifier induced by kernel principal component analysis. RESULTS The proposed retrieval-driven approach was tested on a library of mammogram images from 1006 cases (646 benign and 360 malignant) obtained from multiple institutions and was demonstrated to yield significant improvement in classification performance. Measured by the area under the receiver operating characteristic curve (AUC), the case-adaptive approach could boost the classification performance of a linear classifier from AUC = 0.7415 to AUC = 0.7807; similar improvement was also obtained for a nonlinear classifier, with AUC boosted from 0.7527 to 0.7838. CONCLUSIONS Use of additional cases from a reference library that have similar image features can improve the classification accuracy of a CADx classifier on a query case. It can even outperform retraining the classifier with all the cases from the entire reference library. This implies that cases with similar image features are more relevant in defining the local decision boundary of the CADx classifier around the query.
Collapse
Affiliation(s)
- Hao Jing
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | | | | |
Collapse
|
16
|
Singh S, Maxwell J, Baker JA, Nicholas JL, Lo JY. Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology 2010; 258:73-80. [PMID: 20971779 DOI: 10.1148/radiol.10081308] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classification system. MATERIALS AND METHODS Institutional review board approval was obtained for this HIPAA-compliant study, and the requirement to obtain informed consent was waived. Images of 50 breast lesions were individually interpreted by seven dedicated breast imagers and 10 radiology residents, yielding 850 lesion interpretations. Lesions were described with use of 11 descriptors from the Breast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen κ statistic. Those 11 features were selected, along with patient age, and merged together by a linear discriminant analysis (LDA) classification model trained by using 1005 previously existing cases. Variability in the recommendations of the computer model for different observers was also calculated with the Cohen κ statistic. RESULTS A significant difference was observed for six lesion features, and radiology residents had greater interobserver variability in their selection of five of the six features than did dedicated breast imagers. The LDA model accurately classified lesions for both sets of observers (area under the receiver operating characteristic curve = 0.94 for residents and 0.96 for dedicated imagers). Sensitivity was maintained at 100% for residents and improved from 98% to 100% for dedicated breast imagers. For residents, the computer model could potentially improve the specificity from 20% to 40% (P < .01) and the κ value from 0.09 to 0.53 (P < .001). For dedicated breast imagers, the computer model could increase the specificity from 34% to 43% (P = .16) and the κ value from 0.21 to 0.61 (P < .001). CONCLUSION Among findings showing a significant difference, there was greater interobserver variability in lesion descriptions among residents; however, an LDA model using data from either dedicated breast imagers or residents yielded a consistently high performance in the differentiation of benign from malignant breast lesions, demonstrating potential for improving specificity and decreasing interobserver variability in biopsy recommendations.
Collapse
Affiliation(s)
- Swatee Singh
- Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Rd, Ste 302, Durham, NC 27705, USA.
| | | | | | | | | |
Collapse
|
17
|
Ayer T, Ayvaci MUS, Liu ZX, Alagoz O, Burnside ES. Computer-aided diagnostic models in breast cancer screening. IMAGING IN MEDICINE 2010; 2:313-323. [PMID: 20835372 PMCID: PMC2936490 DOI: 10.2217/iim.10.24] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.
Collapse
Affiliation(s)
- Turgay Ayer
- Industrial & Systems Engineering Department, University of Wisconsin, Madison, WI, USA
| | - Mehmet US Ayvaci
- Industrial & Systems Engineering Department, University of Wisconsin, Madison, WI, USA
| | - Ze Xiu Liu
- Industrial & Systems Engineering Department, University of Wisconsin, Madison, WI, USA
| | - Oguzhan Alagoz
- Industrial & Systems Engineering Department, University of Wisconsin, Madison, WI, USA
- Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA
| | - Elizabeth S Burnside
- Industrial & Systems Engineering Department, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI, USA
| |
Collapse
|
18
|
Antonio ALM, Crespi CM. Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System. Breast Cancer Res Treat 2010; 120:539-46. [PMID: 20300960 DOI: 10.1007/s10549-010-0770-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2010] [Accepted: 01/22/2010] [Indexed: 11/26/2022]
Abstract
The Breast Imaging Reporting and Data System (BI-RADS) was introduced in 1993 to standardize the interpretation of mammograms. Though many studies have assessed the validity of the system, fewer have examined its reliability. Our objective is to identify predictors of reliability as measured by the kappa statistic. We identified studies conducted between 1993 and 2009 which reported kappa values for interpreting mammograms using any edition of BI-RADS. Bivariate and multivariate multilevel analyses were used to examine associations between potential predictors and kappa values. We identified ten eligible studies, which yielded 88 kappa values for the analysis. Potential predictors of kappa included: whether or not the study included negative cases, whether single- or two-view mammograms were used, whether or not mammograms were digital versus screen-film, whether or not the fourth edition of BI-RADS was utilized, the BI-RADS category being evaluated, whether or not readers were trained, whether or not there was an overlap in readers' professional activities, the number of cases in the study and the country in which the study was conducted. Our best multivariate model identified training, use of two-view mammograms and BI-RADS categories (masses, calcifications, and final assessments) as predictors of kappa. Training, use of two-view mammograms and focusing on mass description may be useful in increasing reliability in mammogram interpretation. Calcification and final assessment descriptors are areas for potential improvement. These findings are important for implementing policies in BI-RADS use before introducing the system in different settings and improving current implementations.
Collapse
Affiliation(s)
- Anna Liza M Antonio
- Department of Biostatistics, UCLA School of Public Health, University of California, Los Angeles, CA, 90095-1776, USA.
| | | |
Collapse
|
19
|
Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: A review. Med Phys 2009; 36:2052-68. [PMID: 19610294 DOI: 10.1118/1.3121511] [Citation(s) in RCA: 141] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Matthias Elter
- Fraunhofer Institute for Integrated Circuits, Am Wolfsmantel 33, 91058 Erlangen, Germany.
| | | |
Collapse
|
20
|
Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2009; 35:5799-820. [PMID: 19175137 PMCID: PMC2673617 DOI: 10.1118/1.3013555] [Citation(s) in RCA: 165] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.
Collapse
Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.
| | | | | |
Collapse
|
21
|
Rao RB, Yakhnenko O, Krishnapuram B. KDD cup 2008 and the workshop on mining medical data. ACTA ACUST UNITED AC 2008. [DOI: 10.1145/1540276.1540288] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In this report we summarize the KDD Cup 2008 task, which addressed a problem of early breast cancer detection. We describe the data and the challenges, the results and summarize the algorithms used by the winning teams. We also summarize the workshop on Mining Medical Data held in conjunction with SIGKDD on August 24, 2008 in Las Vegas, NV that brought together researchers working on various aspects of applying machine learning and data mining to challenging tasks in medical and health care domains.
Collapse
|
22
|
Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski L, Helvie MA, Nees A, Wu YT, Wei J, Zhou C, Zhang Y, Cui J. Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys 2008; 35:280-90. [PMID: 18293583 DOI: 10.1118/1.2820630] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based Az value of 0.83 +/- 0.01. The improvement compared to the previous CAD system was statistically significant (p = 0.02). When patient age was included in the new CAD system, view-based and case-based Az values were 0.85 +/- 0.01 and 0.87 +/- 0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening mammography with 132 benign and 197 malignant ROIs containing masses achieved a view-based Az value of 0.84 +/- 0.02.
Collapse
Affiliation(s)
- Jiazheng Shi
- Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904, USA.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|