Browsing by Author "Nair, MS"

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    A novel approach for detection and delineation of cell nuclei using feature similarity index measure
    (BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2016) John, J; Nair, MS; Kumar, PRA; Wilscy, M
    Accurate image segmentation of cells and tissues is a challenging research area due to its vast applications in medical diagnosis. Seed detection is the basic and most essential step for the automated segmentation of microscopic images. This paper presents a robust, accurate and novel method for detecting cell nuclei which can be efficiently used for cell segmentation. We propose a template matching method using a feature similarity index measure (FSIM) for detecting nuclei positions in the image which can be further used as seeds for segmentation tasks. Initially, a Fuzzy C-Means clustering algorithm is applied on the image for separating the foreground region containing the individual and clustered nuclei regions. FSIM based template matching approach is then used for nuclei detection. FSIM makes use of low level texture features for comparisons and hence gives good results. The performance of the proposed method is evaluated on the gold standard dataset containing 36 images (8000 nuclei) of tissue samples and also in vitro cultured cell images of Stromal Fibroblasts (5 images) and Human Macrophage cell line (4 images) using the statistical measures of Precision and Recall. The results are analyzed and compared with other state-of-the-art methods in the literature and software tools to prove its efficiency. Precision is found to be comparable and the Recall rate is found to exceed 92% for the gold standard dataset which shows considerable performance improvement over existing methods. (C) 2015 Nalecz Institute of Biocybemetics and Biomedical Engineering. Published by Elsevier Sp. z o.o. All rights reserved.
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    Cell Image Segmentation and Color Coding based on Nucleus to Cell Ratio
    (2014 INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS), 2014) John, J; Nair, MS; Wilsey, M; Kumar, PRA
    Biomedical analysis is a highly challenging area where lot of research activities is going on. Many automated biomedical image processing procedures have cell segmentation as its first step. Manual methods for this purpose are imprecise, tedious and highly subjective. Hence, novel automated methods are necessary. Analysis of cells includes segmenting the cells as well as computing the area of cell and nucleus. The nucleus to cellular ratio is crucial in determining the type of cells as well as detecting cancerous or damaged cells. In this paper, we propose a novel method to segment nucleus and cell and color code the cells based on their increasing nucleus to cellular ratio. Our tool will help scientists analyze biomedical images in a much better and efficient manner. The results of the proposed method have been compared with the most popular interactive image analysis tool ImageJ. The results obtained using our method is visually more appealing and easier for analysis.