Browsing by Author "Kumar Jain, A.K."

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    A study about color normalization methods for histopathology images
    (Elsevier Ltd, 2018) Roy, S.; Kumar Jain, A.K.; Lal, S.; Kini, J.R.
    Histopathology images are used for the diagnosis of the cancerous disease by the examination of tissue with the help of Whole Slide Imaging (WSI) scanner. A decision support system works well by the analysis of the histopathology images but a lot of problems arise in its decision. Color variation in the histopathology images is occurring due to use of the different scanner, use of various equipments, different stain coloring and reactivity from a different manufacturer. In this paper, detailed study and performance evaluation of color normalization methods on histopathology image datasets are presented. Color normalization of the source image by transferring the mean color of the target image in the source image and also to separate stain present in the source image. Stain separation and color normalization of the histopathology images can be helped for both pathology and computerized decision support system. Quality performances of different color normalization methods are evaluated and compared in terms of quaternion structure similarity index matrix (QSSIM), structure similarity index matrix (SSIM) and Pearson correlation coefficient (PCC) on various histopathology image datasets. Our experimental analysis suggests that structure-preserving color normalization (SPCN) provides better qualitatively and qualitatively results in comparison to the all the presented methods for breast and colorectal cancer histopathology image datasets. © 2018 Elsevier Ltd
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    Feature Extraction of Normalized Colorectal Cancer Histopathology Images
    (Springer Verlag service@springer.de, 2019) Kumar Jain, A.K.; Lal, S.
    This paper presents different types of feature extraction of normalized colorectal cancer histopathology images. These highlights are exceptionally helpful for separating epithelium and stroma in colorectal cancer (CRC) histopathology images. It is also useful for selecting features and its analysis. In this paper, 27 features are extracted in which 5 are the visual texture features and 22 are the other features such as GLCM, run length and intensity-based features to separate epithelium from the stroma of Colorectal Cancer histopathology images. The utilized component has straightforwardly identified with the human recognition which makes it conceivable to distinguish the nearness of tissue based on parameters. The quantity of utilized highlights is little to differentiate the epithelium from the stroma of CRC histopathology images. In the simulation, we use well-defined and verified histopathology images of stroma and epithelium to correctly differentiate epithelium from stroma. The textural features measure provides the excellent result for 16 typical texture patterns. The issue emerges between the human vision, and modernized strategies that are experienced in this examination show the central point in dissecting of the surface. In which, some of them has removed by using better techniques. In conclusion, perception-based features work well in comparison to previously features used. Some modification like colour normalization of epithelium and stroma image and some of the new features are added because the classification of perception-based features is less. © 2019, Springer Nature Singapore Pte Ltd.