Abstract In this paper, we present a novel segmentation framework for glandular structures in Hematoxylin and Eosin stained histology images, choosing poorly differentiated colon tissue as an example. The proposed framework’ target is to identify precise epithelial nuclei objects. We start with staining separate to detect all nuclei objects, and deploy multi-resolution morphology operation to map the initial epithelial nuclei positions. We proposed a new bag of words scheme using sparse random feature to classify epithelial nuclei and stroma nuclei objects to adjust the rest nuclei positions. Finally, we can use the boundary of optimized epithelial nuclei objects to segment the glandular structure.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Novel Segmentation Framework Using Sparse Random Feature in Histology Images of Colon Cancer


    Contributors:
    Zhang, Kun (author) / Zhou, Huiyu (author) / Chen, Li (author) / Fei, Minrui (author) / Wu, Jianguo (author) / Zhang, Peijian (author)


    Publication date :

    2017-01-01


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-band Feature Images Concrete Crack Segmentation Framework Using Deep Learning

    Zhou, Shuang Xi / Pan, Yuan / Guan, Jingyuan et al. | Springer Verlag | 2024


    Stacked Predictive Sparse Decomposition for Classification of Histology Sections

    Chang, H. / Zhou, Y. / Borowsky, A. et al. | British Library Online Contents | 2015


    Segmentation of Images using Automatic Fuzzy Clustering Framework

    Gannavaram V, Tulasi Krishna / Kandhikonda, Uma Maheshwar / Gade, Preetham et al. | IEEE | 2021


    Segmentation of skin cancer images

    Xu, L. / Jackowski, M. / Goshtasby, A. et al. | British Library Online Contents | 1999


    Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

    Alghodhaifi, Hesham / Alghodhaifi, Abdulmajeed / Alghodhaifi, Mohammed | IEEE | 2019