Prof. Sukhwinder Singh Profile

Prof. Sukhwinder Singh

at Panjab Univ

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Publications (2)

Proceedings Article | 20 March 2014 Paper

A content based framework for mass retrieval in mammograms

Simranjit Kaur, Vipul Sharma, Sukhwinder Singh, Savita Gupta

Proceedings Volume 9035, 90351N (2014) https://doi.org/10.1117/12.2043354

KEYWORDS: Image segmentation, Feature extraction, Mammography, Pathology, Feature selection, Breast, Image retrieval, Databases, Shape analysis, Tissues

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Proceedings Article | 26 February 2013 Paper

Comparison of texture models for efficient ultrasound image retrieval

Maggi Bansal, Vipul Sharma, Sukhwinder Singh

Proceedings Volume 8670, 86702C (2013) https://doi.org/10.1117/12.2007837

KEYWORDS: Liver, Image retrieval, Performance modeling, Ultrasonography, Databases, Feature extraction, Medical imaging, Visualization, Fractal analysis, Image analysis

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Due to availability of inexpensive and easily available image capturing devices, the size of digital image collection is increasing rapidly. Thus, there is need to create efficient access methods or retrieval tools to search, browse and retrieve images from large multimedia repositories. More specifically, researchers have been engaged on different ways of retrieving images based on their actual content. In particular, Content Based Image Retrieval (CBIR) systems have attracted considerable research and commercial interest in the recent years. In CBIR, visual features characterizing the image content are color, shape and texture. Currently, texture is used to quantify the image content of medical images as it is the most prominent feature that contains information about the spatial distribution of gray levels and variations in brightness. Various texture models like Haralick’s Spatial Gray Level Co-occurence Matrix (SGLCM), Gray Level Difference Statistics (GLDS), First-order Statistics (FoS), Statistical Feature Matrix (SFM), Law’s Texture Energy Measures (TEM), Fractal features and Fourier Power Spectrum (FPS) features exists in literature. Each of these models visualizes texture in a different way. Retrieval performance depends upon the choice of texture algorithm. Unfortunately, there is no texture model known to work best for encoding texture properties of liver ultrasound images or retrieving most similar images. An experimental comparison of different texture models for Content Based Medical Image Retrieval (CBMIR) is presented in this paper. For the experiments, liver ultrasound image database is used and the retrieval performance of the various texture models is analyzed in detail. The paper concludes with recommendations which texture model performs better for liver ultrasound images. Interestingly, FPS and SGLCM based Haralick’s features perform well for liver ultrasound retrieval and thus can be recommended as a simple baseline for such images.

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