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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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2
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Su CH, Chung PC, Lin SF, Tsai HW, Yang TL, Su YC. Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22072679. [PMID: 35408293 PMCID: PMC9003085 DOI: 10.3390/s22072679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 05/07/2023]
Abstract
In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether portal areas have fibrous expansion, bridging with other portal areas, or bridging with central veins. Based on these staging criteria, it is necessary to identify portal areas and central veins when performing the Ishak Score staging. The bile ducts have variant types and are very difficult to be detected under a single magnification, hence pathologists must observe bile ducts at different magnifications to obtain sufficient information. This pathologic examinations in routine clinical practice, however, would result in the labor intensive and expensive examination process. Therefore, the automatic quantitative analysis for pathologic examinations has had an increased demand and attracted significant attention recently. A multi-scale inputs of attention convolutional network is proposed in this study to simulate pathologists' examination procedure for observing bile ducts under different magnifications in liver biopsy. The proposed multi-scale attention network integrates cell-level information and adjacent structural feature information for bile duct segmentation. In addition, the attention mechanism of proposed model enables the network to focus the segmentation task on the input of high magnification, reducing the influence from low magnification input, but still helps to provide wider field of surrounding information. In comparison with existing models, including FCN, U-Net, SegNet, DeepLabv3 and DeepLabv3-plus, the experimental results demonstrated that the proposed model improved the segmentation performance on Masson bile duct segmentation task with 72.5% IOU and 84.1% F1-score.
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Affiliation(s)
- Chun-Han Su
- Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan City 701, Taiwan; (C.-H.S.); (P.-C.C.)
| | - Pau-Choo Chung
- Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan City 701, Taiwan; (C.-H.S.); (P.-C.C.)
| | - Sheng-Fung Lin
- Division of Hematology and Oncology, Department of Internal Medicine, E-Da Hospital, Kaohsiung 824, Taiwan;
| | - Hung-Wen Tsai
- Department of Pathology, National Cheng Kung University Hospital, Tainan City 704, Taiwan;
| | - Tsung-Lung Yang
- Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Yu-Chieh Su
- Division of Hematology and Oncology, Department of Internal Medicine, E-Da Hospital, Kaohsiung 824, Taiwan;
- School of Medicine, I-Shou University, Kaohsiung 824, Taiwan
- Correspondence:
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3
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Xu H, Liu L, Lei X, Mandal M, Lu C. An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Comput Med Imaging Graph 2021; 93:101974. [PMID: 34481236 DOI: 10.1016/j.compmedimag.2021.101974] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/11/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.
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Affiliation(s)
- Hongming Xu
- School of Biomedical Engineering at Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Lina Liu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Xiujuan Lei
- College of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Mrinal Mandal
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
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4
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Van Eycke YR, Balsat C, Verset L, Debeir O, Salmon I, Decaestecker C. Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach. Med Image Anal 2018; 49:35-45. [PMID: 30081241 DOI: 10.1016/j.media.2018.07.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 06/29/2018] [Accepted: 07/05/2018] [Indexed: 12/18/2022]
Abstract
In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC. The network only needs to be fine-tuned on a small number of additional examples to be accurate on a new IHC dataset. Our approach also includes a new method of data augmentation to achieve good generalisation when working with different experimental conditions and different IHC markers. We show that our methodology enables to automate the compartmentalisation of the IHC biomarker analysis, results concurring highly with manual annotations.
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Affiliation(s)
- Yves-Rémi Van Eycke
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), CPI 305/1, Rue Adrienne Bolland, 8, 6041 Gosselies, Belgium; Laboratories of Image, Signal processing & Acoustics, Université Libre de Bruxelles (ULB), CPI 165/57, Avenue Franklin Roosevelt 50, Brussels 1050 , Belgium.
| | - Cédric Balsat
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), CPI 305/1, Rue Adrienne Bolland, 8, 6041 Gosselies, Belgium
| | - Laurine Verset
- Department of Pathology, Erasme Hospital, Université Libre de Bruxelles (ULB), Route de Lennik 808, Brussels 1070, Belgium
| | - Olivier Debeir
- Laboratories of Image, Signal processing & Acoustics, Université Libre de Bruxelles (ULB), CPI 165/57, Avenue Franklin Roosevelt 50, Brussels 1050 , Belgium; MIP, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), CPI 305/1, Rue Adrienne Bolland, 8, 6041 Gosselies, Belgium
| | - Isabelle Salmon
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), CPI 305/1, Rue Adrienne Bolland, 8, 6041 Gosselies, Belgium; Department of Pathology, Erasme Hospital, Université Libre de Bruxelles (ULB), Route de Lennik 808, Brussels 1070, Belgium
| | - Christine Decaestecker
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), CPI 305/1, Rue Adrienne Bolland, 8, 6041 Gosselies, Belgium; Laboratories of Image, Signal processing & Acoustics, Université Libre de Bruxelles (ULB), CPI 165/57, Avenue Franklin Roosevelt 50, Brussels 1050 , Belgium.
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5
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Banwari A, Sengar N, Dutta MK. Image Processing Based Colorectal Cancer Detection in Histopathological Images. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018040101] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The article proposes an image processing-based automatic methodology for early diagnosis of colorectal cancer. In pathology, staining and sectioning of tissues are routinely used as a primary technique to detect cancer. In this methodology, the colorectal gland tissues are segmented by using adaptive threshold method. Also, it includes an analysis of geometrical features of colorectal tissues as well as it does classification of cancerous cells which classify the cancerous and non-cancerous cell efficiently. The classification is based on discriminatory geometrical features which gives good result. Unlike existing methods, it quantifies lumen and epithelial cells only in the ROI, which makes this method computationally efficient. Automatic supervised classification is accomplished on the extracted discriminatory features using support vector machine classifier. The proposed methodology segments and classifies the cancerous / non-cancerous region with an accuracy of 93.74%. The proposed method is also computationally fast which makes it suitable for real time applications.
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6
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Epithelium and Stroma Identification in Histopathological Images Using Unsupervised and Semi-Supervised Superpixel-Based Segmentation. J Imaging 2017. [DOI: 10.3390/jimaging3040061] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
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7
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Nguyen L, Tosun AB, Fine JL, Taylor DL, Chennubhotla SC. ARCHITECTURAL PATTERNS FOR DIFFERENTIAL DIAGNOSIS OF PROLIFERATIVE BREAST LESIONS FROM HISTOPATHOLOGICAL IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2017; 2017:152-155. [PMID: 28890755 DOI: 10.1109/isbi.2017.7950490] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic boundaries on features that otherwise exist on a continuum going from benign to atypia to malignant is a challenge. Current computational pathology methods have focused primarily on nuclear atypia in drawing these boundaries. In this paper, we improve on these approaches by encoding for both cellular morphology and spatial architectural patterns. Using a publicly available breast lesion database consisting of UDH and three different grades of DCIS, we improve the classification accuracy by 10% over the state-of-the-art method for discriminating UDH and DCIS. For the four way classification of UDH and the three grades of DCIS, our method improves the results by 6% in accuracy, 8% in micro-AUC, and 19% in macro-AUC.
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Affiliation(s)
- L Nguyen
- Department of Computational and Systems Biology, University of Pittsburgh.,Drug Discovery Institute, University of Pittsburgh
| | - A B Tosun
- Department of Computational and Systems Biology, University of Pittsburgh
| | - J L Fine
- Magee Womens Hospital of UPMC and Department of Pathology, University of Pittsburgh
| | - D L Taylor
- Department of Computational and Systems Biology, University of Pittsburgh.,Drug Discovery Institute, University of Pittsburgh
| | - S C Chennubhotla
- Department of Computational and Systems Biology, University of Pittsburgh.,Drug Discovery Institute, University of Pittsburgh
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8
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Mazo C, Trujillo M, Alegre E, Salazar L. Automatic recognition of fundamental tissues on histology images of the human cardiovascular system. Micron 2016; 89:1-8. [DOI: 10.1016/j.micron.2016.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Revised: 05/25/2016] [Accepted: 07/05/2016] [Indexed: 10/21/2022]
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9
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Sokouti M, Sokouti B. ARTIFICIAL INTELLIGENT SYSTEMS APPLICATION IN CERVICAL CANCER PATHOLOGICAL CELL IMAGE CLASSIFICATION SYSTEMS — A REVIEW. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2016. [DOI: 10.4015/s1016237216300017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cervical cancer cell images play an important part in diagnosing the cancer among the females worldwide. Existing noises, overlapping cells, mucus, blood and air artifacts in cervical cancer cell images makes their classification a hard task. It makes it difficult for both pathologists and intelligent systems to segment and classify them into normal, pre-cancerous and cancerous cells. However, true cell segmentation is needed for pathologists to make for accurate diagnosis. In this paper, a review of algorithms used for cervical cancer cell image classification is presented. This includes pre-processing steps (noise reduction and cell segmentation/without segmentation), feature extraction, and intelligent diagnosis systems and their evaluations. Finally, future research trends on cervical cell classification to achieve complete accuracy are described.
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Affiliation(s)
- Massoud Sokouti
- Nuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
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10
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Belsare AD, Mushrif MM, Pangarkar MA, Meshram N. Breast histopathology image segmentation using spatio-colour-texture based graph partition method. J Microsc 2015; 262:260-73. [PMID: 26708167 DOI: 10.1111/jmi.12361] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 11/17/2015] [Indexed: 01/10/2023]
Abstract
This paper proposes a novel integrated spatio-colour-texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin-Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio-colour-texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground-truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods.
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Affiliation(s)
- A D Belsare
- Department of Electronics & Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
| | - M M Mushrif
- Department of Electronics & Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
| | - M A Pangarkar
- Department of Pathology, Government Medical College and Hospital, Nagpur
| | - N Meshram
- Department of Pathology, Government Medical College and Hospital, Nagpur
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11
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Olgun G, Sokmensuer C, Gunduz-Demir C. Local Object Patterns for the Representation and Classification of Colon Tissue Images. IEEE J Biomed Health Inform 2014; 18:1390-6. [DOI: 10.1109/jbhi.2013.2281335] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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12
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McCann MT, Mixon DG, Fickus MC, Castro CA, Ozolek JA, Kovacevic J. Images as occlusions of textures: a framework for segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2014; 23:2033-2046. [PMID: 24710403 DOI: 10.1109/tip.2014.2307475] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.
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13
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Rathore S, Hussain M, Ali A, Khan A. A recent survey on colon cancer detection techniques. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:545-63. [PMID: 24091390 DOI: 10.1109/tcbb.2013.84] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.
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Affiliation(s)
- Saima Rathore
- DCIS, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad and University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir
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Chaudhury B, Ahmady Phoulady H, Goldgof D, Hall LO, Mouton PR, Hakam A, Siegel EM. An Ensemble Algorithm Framework for Automated Stereology of Cervical Cancer. IMAGE ANALYSIS AND PROCESSING – ICIAP 2013 2013:823-832. [DOI: 10.1007/978-3-642-41181-6_83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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