1
|
Tupper LL, Keese CR, Matteson DS. Classifying contaminated cell cultures using time series features. J Appl Stat 2023; 51:1210-1226. [PMID: 38628445 PMCID: PMC11018005 DOI: 10.1080/02664763.2023.2248413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 07/30/2023] [Indexed: 04/19/2024]
Abstract
We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.
Collapse
|
2
|
Fekri-Ershad S, Al-Imari MJ, Hamad MH, Alsaffar MF, Hassan FG, Hadi ME, Mahdi KS. Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6895833. [PMID: 36479023 PMCID: PMC9722294 DOI: 10.1155/2022/6895833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 11/05/2022] [Accepted: 11/16/2022] [Indexed: 11/30/2022]
Abstract
Cell phenotype classification is a critical task in many medical applications, such as protein localization, gene effect identification, and cancer diagnosis in some types. Fluorescence imaging is the most efficient tool to analyze the biological characteristics of cells. So cell phenotype classification in fluorescence microscopy images has received increased attention from scientists in the last decade. The visible structures of cells are usually different in terms of shape, texture, relationship between intensities, etc. In this scope, most of the presented approaches use one type or joint of low-level and high-level features. In this paper, a new approach is proposed based on a combination of low-level and high-level features. An improved version of local quinary patterns is used to extract low-level texture features. Also, an innovative multilayer deep feature extraction method is performed to extract high-level features from DenseNet. In this respect, an output feature map of dense blocks is entered in a separate way to pooling and flatten layers, and finally, feature vectors are concatenated. The performance of the proposed approach is evaluated on the benchmark dataset 2D-HeLa in terms of accuracy. Also, the proposed approach is compared with state-of-the-art methods in terms of classification accuracy. Comparison of results demonstrates higher performance of the proposed approach in comparison with some efficient methods.
Collapse
Affiliation(s)
- Shervan Fekri-Ershad
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Mustafa Jawad Al-Imari
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| | - Mohammed Hayder Hamad
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| | - Marwa Fadhil Alsaffar
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| | - Fuad Ghazi Hassan
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| | - Mazin Eidan Hadi
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| | - Karrar Salih Mahdi
- Department of Medical Laboratory Techniques, Al-Mustaqbal University College, Hillah 51001, Babylon, Iraq
| |
Collapse
|
3
|
Deep localization of subcellular protein structures from fluorescence microscopy images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06715-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
4
|
Liu GH, Zhang BW, Qian G, Wang B, Mao B, Bichindaritz I. Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1966-1980. [PMID: 31107658 DOI: 10.1109/tcbb.2019.2917429] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.
Collapse
|
5
|
Schormann W, Hariharan S, Andrews DW. A reference library for assigning protein subcellular localizations by image-based machine learning. J Cell Biol 2020; 219:133635. [PMID: 31968357 PMCID: PMC7055006 DOI: 10.1083/jcb.201904090] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 09/30/2019] [Accepted: 12/15/2019] [Indexed: 12/11/2022] Open
Abstract
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morphology and statistical features were measured. Machine learning algorithms using these features permit automated assignment of the localization of other proteins and dyes in both cell types with very high accuracy. Automated assignment of subcellular localizations for model tail-anchored proteins with randomly mutated C-terminal targeting sequences allowed the discovery of motifs responsible for targeting to mitochondria, endoplasmic reticulum, and the late secretory pathway. Analysis of directed mutants enabled refinement of these motifs and characterization of protein distributions in within cellular subcompartments.
Collapse
Affiliation(s)
- Wiebke Schormann
- Biological Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - David W Andrews
- Biological Sciences, Sunnybrook Research Institute, Toronto, Canada.,Department of Biochemistry, University of Toronto, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| |
Collapse
|
6
|
Navdeep, Singh V, Rani A, Goyal S. An improved hyper smoothing function based edge detection algorithm for noisy images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179713] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Navdeep
- Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India
| | - Vijander Singh
- Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India
| | - Asha Rani
- Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India
| | - Sonal Goyal
- Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India
| |
Collapse
|
7
|
Nagao Y, Sakamoto M, Chinen T, Okada Y, Takao D. Robust classification of cell cycle phase and biological feature extraction by image-based deep learning. Mol Biol Cell 2020; 31:1346-1354. [PMID: 32320349 PMCID: PMC7353138 DOI: 10.1091/mbc.e20-03-0187] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.
Collapse
Affiliation(s)
- Yukiko Nagao
- Faculty of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Mika Sakamoto
- Genome Informatics Laboratory, National Institute of Genetics, Mishima 411-8540, Japan
| | - Takumi Chinen
- Faculty of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yasushi Okada
- Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan.,Department of Physics and Universal Biology Institute (UBI), Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.,Laboratory for Cell Polarity Regulation, Center for Biosystems Dynamics Research (BDR), RIKEN, Osaka 565-0874, Japan
| | - Daisuke Takao
- Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| |
Collapse
|
8
|
Local bit-plane decoded convolutional neural network features for biomedical image retrieval. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04279-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
9
|
Giacalone M, Rasti P, Debs N, Frindel C, Cho TH, Grenier E, Rousseau D. Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Med Image Anal 2018; 50:117-126. [PMID: 30268970 DOI: 10.1016/j.media.2018.08.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 07/28/2018] [Accepted: 08/31/2018] [Indexed: 10/28/2022]
Abstract
We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel. As a first illustration for this approach, the textures are characterized with local binary patterns and the classification is performed using a standard support vector machine (SVM). This simple machine learning classification scheme demonstrates results with 95% accuracy on average while working only raw perfusion data. We discuss the influence of the observation scale and evaluate the interest of using post-processed perfusion data with this approach.
Collapse
Affiliation(s)
- Mathilde Giacalone
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Pejman Rasti
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France
| | - Noelie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Emmanuel Grenier
- ENS-Lyon, UMR CNRS 5669 'UMPA', and INRIA Alpes, project NUMED, Lyon F-69364, France
| | - David Rousseau
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France.
| |
Collapse
|
10
|
Deep G, Kaur L, Gupta S. Local quantized extrema quinary pattern: a new descriptor for biomedical image indexing and retrieval. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2017. [DOI: 10.1080/21681163.2017.1344933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- G. Deep
- Department of Computer Science & Engineering, Chandigarh Engineering College, Landran, Mohali, India
| | - L. Kaur
- Department of CE, Punjabi University (Pb.), Patiala, India
| | - S. Gupta
- Department of CSE, UIET, PU, Chandigarh, India
| |
Collapse
|
11
|
Ansari MD, Ghrera SP, Mishra AR. Texture Feature Extraction Using Intuitionistic Fuzzy Local Binary Pattern. JOURNAL OF INTELLIGENT SYSTEMS 2016. [DOI: 10.1515/jisys-2016-0155] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Abstract
In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256×256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.
Collapse
Affiliation(s)
- Mohd Dilshad Ansari
- Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India
| | - Satya Prakash Ghrera
- Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India
| | | |
Collapse
|
12
|
Deep G, Kaur L, Gupta S. Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1193447] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- G. Deep
- Department of CSE, IET Bhaddal, Punjab Technical University, Ropar, India
| | - L. Kaur
- Department of CE, Punjabi University(Pb.), Patiala, India
| | - S. Gupta
- Department of CSE, UIET, PU, Chandigarh, India
| |
Collapse
|
13
|
Niwas SI, Jakhetiya V, Lin W, Kwoh CK, Sng CC, Aquino MC, Victor K, Chew PTK. Complex wavelet based quality assessment for AS-OCT images with application to Angle Closure Glaucoma diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:13-21. [PMID: 27208517 DOI: 10.1016/j.cmpb.2016.03.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 02/14/2016] [Accepted: 03/08/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Angle closure disease in the eye can be detected using time-domain Anterior Segment Optical Coherence Tomography (AS-OCT). The Anterior Chamber (AC) characteristics can be quantified from AS-OCT image, which is dependent on the image quality at the image acquisition stage. To date, to the best of our knowledge there are no objective or automated subjective measurements to assess the quality of AS-OCT images. METHODS To address AS-OCT image quality assessment issue, we define a method for objective assessment of AS-OCT images using complex wavelet based local binary pattern features. These features are pooled using the Naïve Bayes classifier to obtain the final quality parameter. To evaluate the proposed method, a subjective assessment has been performed by clinical AS-OCT experts, who graded the quality of AS-OCT images on a scale of good, fair, and poor. This was done based on the ability to identify the AC structures including the position of the scleral spur. RESULTS We compared the results of the proposed objective assessment with the subjective assessments. From this comparison, it is validated that the proposed objective assessment has the ability of differentiating the good and fair quality AS-OCT images for glaucoma diagnosis from the poor quality AS-OCT images. CONCLUSIONS This proposed algorithm is an automated approach to evaluate the AS-OCT images with the intention for collecting of high quality data for further medical diagnosis. Our proposed quality index has the ability of automatic objective and quantitative assessment of AS-OCT image quality and this quality index is similar to glaucoma specialist.
Collapse
Affiliation(s)
- Swamidoss Issac Niwas
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Vinit Jakhetiya
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore; Department of Electronics and Computer Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong.
| | - Weisi Lin
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Chee Keong Kwoh
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Chelvin C Sng
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
| | | | - Koh Victor
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
| | - Paul T K Chew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
| |
Collapse
|
14
|
Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.01.064] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
15
|
Yang Q, Zou HY, Zhang Y, Tang LJ, Shen GL, Jiang JH, Yu RQ. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm. Talanta 2016; 147:609-14. [DOI: 10.1016/j.talanta.2015.10.047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/14/2015] [Accepted: 10/18/2015] [Indexed: 11/30/2022]
|
16
|
Abbas SS, Dijkstra TMH, Heskes T. A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinformatics 2014; 15:342. [PMID: 25336059 PMCID: PMC4287552 DOI: 10.1186/1471-2105-15-342] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Accepted: 09/29/2014] [Indexed: 11/24/2022] Open
Abstract
Background Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells. Results For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42 %. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41 % better in performance than SVM (linear). Conclusions Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-342) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Syed Saiden Abbas
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands.
| | | | | |
Collapse
|
17
|
Yang F, Xu YY, Shen HB. Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification? ScientificWorldJournal 2014; 2014:429049. [PMID: 25050396 PMCID: PMC4094881 DOI: 10.1155/2014/429049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Accepted: 05/22/2014] [Indexed: 01/14/2023] Open
Abstract
Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.
Collapse
Affiliation(s)
- Fan Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Optic-Electronic and Communication, Jiangxi Science & Technology Normal University, Nanchang 330013, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Ying-Ying Xu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| |
Collapse
|
18
|
Yang F, Xu YY, Wang ST, Shen HB. Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.10.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
19
|
Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. J Theor Biol 2014; 340:85-95. [DOI: 10.1016/j.jtbi.2013.08.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 07/09/2013] [Accepted: 08/15/2013] [Indexed: 11/21/2022]
|
20
|
Different approaches for extracting information from the co-occurrence matrix. PLoS One 2013; 8:e83554. [PMID: 24386228 PMCID: PMC3873395 DOI: 10.1371/journal.pone.0083554] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 11/05/2013] [Indexed: 02/06/2023] Open
Abstract
In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.
Collapse
|
21
|
Lee TC, Lin YH, Uedo N, Wang HP, Chang HT, Hung CW. Computer-aided diagnosis in endoscopy: a novel application toward automatic detection of abnormal lesions on magnifying narrow-band imaging endoscopy in the stomach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:4430-3. [PMID: 24110716 DOI: 10.1109/embc.2013.6610529] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Gastric cancer is the fourth common cancer and the second major cause of cancer death worldwide. Early detection of gastric cancer by endoscopy surveillance is actively investigated to improve patient survival, especially using the newly developed magnifying narrow-band imaging endoscopy in the stomach. However, meticulous examination of the aforementioned images is both time and experience demanding and interpretation could be variable among different doctors, which hindered its widespread application. In this study, we developed a new image analysis system by adopting local binary pattern and vector quantization to perform pattern comparison between known training abnormal images and testing images of magnifying narrow band endoscopy images in the stomach. Our preliminary results demonstrated promising potential for automatically labeled region of interest for endoscopy doctors to focus on abnormal lesions for subsequent targeted biopsy, with the rates of recall 0.46-1.00 and precision 0.39-0.87.
Collapse
|
22
|
Coelho LP, Kangas JD, Naik AW, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget PB, Jarvik JW, Murphy RF. Determining the subcellular location of new proteins from microscope images using local features. ACTA ACUST UNITED AC 2013; 29:2343-9. [PMID: 23836142 DOI: 10.1093/bioinformatics/btt392] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. RESULTS Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. AVAILABILITY The datasets are available for download at http://murphylab.web.cmu.edu/data/. The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address. CONTACT murphy@cmu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Luis Pedro Coelho
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | | | | | | | | | | | | | | | | | | |
Collapse
|
23
|
Handfield LF, Chong YT, Simmons J, Andrews BJ, Moses AM. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput Biol 2013; 9:e1003085. [PMID: 23785265 PMCID: PMC3681667 DOI: 10.1371/journal.pcbi.1003085] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Accepted: 04/19/2013] [Indexed: 12/11/2022] Open
Abstract
Protein subcellular localization has been systematically characterized in budding yeast using fluorescently tagged proteins. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches that have been trained to recognize predefined image classes based on statistical features. Here, we present an unsupervised analysis of protein expression patterns in a set of high-resolution, high-throughput microscope images. Our analysis is based on 7 biologically interpretable features which are evaluated on automatically identified cells, and whose cell-stage dependency is captured by a continuous model for cell growth. We show that it is possible to identify most previously identified localization patterns in a cluster analysis based on these features and that similarities between the inferred expression patterns contain more information about protein function than can be explained by a previous manual categorization of subcellular localization. Furthermore, the inferred cell-stage associated to each fluorescence measurement allows us to visualize large groups of proteins entering the bud at specific stages of bud growth. These correspond to proteins localized to organelles, revealing that the organelles must be entering the bud in a stereotypical order. We also identify and organize a smaller group of proteins that show subtle differences in the way they move around the bud during growth. Our results suggest that biologically interpretable features based on explicit models of cell morphology will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images. The location of a particular protein in the cell is one of the most important pieces of information that cell biologists use to understand its function. Fluorescent tags are a powerful way to determine the location of a protein in living cells. Nearly a decade ago, a collection of yeast strains was introduced, where in each strain a single protein was tagged with green fluorescent protein (GFP). Here, we show that by training a computer to accurately identify the buds of growing yeast cells, and then making simple fluorescence measurements in context of cell shape and cell stage, the computer could automatically discover most of the localization patterns (nucleus, cytoplasm, mitochondria, etc.) without any prior knowledge of what the patterns might be. Because we made the same, simple measurements for each yeast cell, we could compare and visualize the patterns of fluorescence for the entire collection of strains. This allowed us to identify large groups of proteins moving around the cell in a coordinated fashion, and to identify new, complex patterns that had previously been difficult to describe.
Collapse
Affiliation(s)
| | - Yolanda T. Chong
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Jibril Simmons
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
| | - Brenda J. Andrews
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Alan M. Moses
- Department of Computer Science, University of Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
- * E-mail:
| |
Collapse
|
24
|
|
25
|
Rajaram S, Pavie B, Wu LF, Altschuler SJ. PhenoRipper: software for rapidly profiling microscopy images. Nat Methods 2012; 9:635-7. [PMID: 22743764 DOI: 10.1038/nmeth.2097] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
26
|
Linder N, Konsti J, Turkki R, Rahtu E, Lundin M, Nordling S, Haglund C, Ahonen T, Pietikäinen M, Lundin J. Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn Pathol 2012; 7:22. [PMID: 22385523 PMCID: PMC3315400 DOI: 10.1186/1746-1596-7-22] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Accepted: 03/02/2012] [Indexed: 11/22/2022] Open
Abstract
Background The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images. Results The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, P < 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively. Conclusions The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537
Collapse
Affiliation(s)
- Nina Linder
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | | | | | | | | | | | | | | | | | | |
Collapse
|
27
|
Nanni L, Lumini A. Ensemble of Neural Networks for Automated Cell Phenotype Image Classification. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.
Collapse
|
28
|
Tahir M, Khan A, Majid A. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics 2011; 28:91-7. [DOI: 10.1093/bioinformatics/btr624] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
|
29
|
Nanni L, Lumini A, Brahnam S. Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 2010; 49:117-25. [PMID: 20338737 DOI: 10.1016/j.artmed.2010.02.006] [Citation(s) in RCA: 153] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2009] [Revised: 02/23/2010] [Accepted: 02/27/2010] [Indexed: 11/28/2022]
Abstract
OBJECTIVE This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine). METHODS AND MATERIALS Extensive experiments are conducted using the following three datasets: RESULTS AND CONCLUSION Our results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets. EQP is based on an elliptic neighborhood and a 5 levels scale for encoding the local gray-scale difference. Particularly interesting are the results on the widely studied 2D-HeLa dataset, where, to the best of our knowledge, the proposed descriptor obtains the highest performance among all the several texture descriptors tested in the literature.
Collapse
Affiliation(s)
- Loris Nanni
- Department of Electronic, Informatics and Systems, Università di Bologna, Via Venezia 52, 47023 Cesena, Italy.
| | | | | |
Collapse
|
30
|
Misselwitz B, Strittmatter G, Periaswamy B, Schlumberger MC, Rout S, Horvath P, Kozak K, Hardt WD. Enhanced CellClassifier: a multi-class classification tool for microscopy images. BMC Bioinformatics 2010; 11:30. [PMID: 20074370 PMCID: PMC2821321 DOI: 10.1186/1471-2105-11-30] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2009] [Accepted: 01/14/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. RESULTS We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables. CONCLUSION Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.
Collapse
|
31
|
Novel features for automated cell phenotype image classification. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2010; 680:207-13. [PMID: 20865503 DOI: 10.1007/978-1-4419-5913-3_24] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter, we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg-Marquardt neural networks. The process requires that we first run several experiments to determine the individual features that offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach, we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein subcellular localization databases.
Collapse
|