Review on Histopathological Slide Analysis using Digital Microscopy

Bhattacharjee, Sangita; Mukherjee, Jashojit; Nag, Sanjay; Maitra, Indra Kanta; Bandyopadhyay, Samir Kumar

doi:10.14257/ijast.2014.62.06

Cited by 33 publications

(14 citation statements)

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“…Preprocessing is considered to be one of the elementary steps in image research, which will ease the user to make the image representation in such a way that the application of algorithms will be much easier for various other operations such as segmentation, feature extraction and so on [11][12] [19]. The separation of foreground from the background is a vital part for image processing and computer vision as it reduces the computational resources utilized [13].…”

Section: A Conventional Techniquesmentioning

confidence: 99%

TADOC : Tool for Automated Detection of Oral Cancer

Sattar¹

2020

IJACSA

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Cancer is a group of related diseases and it is necessary to classify the type and its impact. In this paper an automated learning-based system for detection of oral cancer from Whole Slide Images (WSI) has been designed. The main challenges of the system were to handle the huge dataset and to train the machine learning model as it consumed more time for each iteration involved. This further increased the time consumed to get a proper model and decrease of freedom for experimentation. Other important key features of the system were to implement a futuristic deep learning architecture to classify small patches from the large whole slide images and use of carefully designed post-processing methods for the slide-based classification.

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Section: A Conventional Techniquesmentioning

confidence: 99%

TADOC : Tool for Automated Detection of Oral Cancer

Sattar¹

2020

IJACSA

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“…The techniques cover all steps of a computer vision system, i.e. preprocessing, segmentation and classification [29,30]. WSI processing is slowly becoming more relevant for clinical trials, to ensure proper classification of cases and to lower diagnosis discrepancy rates.…”

Section: Image Analysis In Digital Pathologymentioning

confidence: 99%

New Trends of Emerging Technologies in Digital Pathology

et al. 2016

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The future paradigm of pathology will be digital. Instead of conventional microscopy, a pathologist will perform a diagnosis through interacting with images on computer screens and performing quantitative analysis. The fourth generation of virtual slide telepathology systems, so-called virtual microscopy and whole-slide imaging (WSI), has allowed for the storage and fast dissemination of image data in pathology and other biomedical areas. These novel digital imaging modalities encompass high-resolution scanning of tissue slides and derived technologies, including automatic digitization and computational processing of whole microscopic slides. Moreover, automated image analysis with WSI can extract specific diagnostic features of diseases and quantify individual components of these features to support diagnoses and provide informative clinical measures of disease. Therefore, the challenge is to apply information technology and image analysis methods to exploit the new and emerging digital pathology technologies effectively in order to process and model all the data and information contained in WSI. The final objective is to support the complex workflow from specimen receipt to anatomic pathology report transmission, that is, to improve diagnosis both in terms of pathologists' efficiency and with new information. This article reviews the main concerns about and novel methods of digital pathology discussed at the latest workshop in the field carried out within the European project AIDPATH (Academia and Industry Collaboration for Digital Pathology).

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“…They presented a cellular level diagnosis system using image processing techniques. Bhattacharjee et al [ 11 ] presented a review on computer aided diagnosis system to detect cancer from microscopic biopsy images using image processing techniques.…”

Section: Related Workmentioning

confidence: 99%

Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

Kumar

Srivastava

2015

Journal of Medical Engineering

185

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A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

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Review on Histopathological Slide Analysis using Digital Microscopy

Abstract: Abstract

Cited by 33 publications

References 50 publications

TADOC : Tool for Automated Detection of Oral Cancer

TADOC : Tool for Automated Detection of Oral Cancer

New Trends of Emerging Technologies in Digital Pathology

Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

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