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J. Imaging 2018, 4(3), 46; https://doi.org/10.3390/jimaging4030046

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh-17005, India
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK, 11–13 July 2017.
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Received: 7 November 2017 / Revised: 9 February 2018 / Accepted: 16 February 2018 / Published: 26 February 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection. View Full-Text
Keywords: feature selection; HEp-2 cell image classification; filter methods; hybridization; random forest; class-specific features feature selection; HEp-2 cell image classification; filter methods; hybridization; random forest; class-specific features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gupta, V.; Bhavsar, A. Feature Importance for Human Epithelial (HEp-2) Cell Image Classification. J. Imaging 2018, 4, 46.

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