Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning

Tiep Huu Vu, Hojjat Seyed Mousavi, Vishal Monga, Ganesh Rao, U. K.Arvind Rao

    Research output: Contribution to journalArticlepeer-review

    105 Scopus citations

    Abstract

    In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

    Original languageEnglish (US)
    Article number7303944
    Pages (from-to)738-751
    Number of pages14
    JournalIEEE transactions on medical imaging
    Volume35
    Issue number3
    DOIs
    StatePublished - Mar 2016

    All Science Journal Classification (ASJC) codes

    • Software
    • Radiological and Ultrasound Technology
    • Computer Science Applications
    • Electrical and Electronic Engineering

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