The 6th 2013 Biomedical Engineering International Conference 2013
DOI: 10.1109/bmeicon.2013.6687634
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Preliminary results of breast cancer cell classifying based on gray-level co-occurrence matrix

Abstract: This study proposes and appraise a gray level cooccurrence matrix (GLCM) for extracting the feature of cell regions in microscopic image into four region types: positive cancer cell, negative cancer cell, lymphocyte and stromal cell. The classification task uses decision tree with cross validation. To give a high classification performance, the main focus of interest is feature extraction task. Twenty-two texture features of GLCM have used to analysis images at four directions and six scales of gray-level quan… Show more

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“…There are many works dedicated to object recognition for Content Based Information Retrieval (CBIR) on histological images. Some of them using techniques based on: (i) texture, many works have compared several feature types on healthy and pathological histological images demonstrating a clear improvement of the algorithms performance and classification process (Markkongkeaw et al, 2013;Peyret et al, 2015;Lai et al, 2011); (ii) shape-based features may capture the particular characteristics to recognise cells or some pathologies -tumours or cancer -, some results suggest that these shapes are not accurate diagnostic features but it can be used for specific aims (Kothari et al, 2013;Melnyk, 2015;Sharma et al, 2012); (iii) colour representation may be used for diagnosis diseases and as an sign to recognise cells or tissues. However, the colour of histological images may vary according to the stain, the preparation procedure 2.…”
Section: Feature Selection and Machine Learning Algorithms Applied Tomentioning
confidence: 99%
“…There are many works dedicated to object recognition for Content Based Information Retrieval (CBIR) on histological images. Some of them using techniques based on: (i) texture, many works have compared several feature types on healthy and pathological histological images demonstrating a clear improvement of the algorithms performance and classification process (Markkongkeaw et al, 2013;Peyret et al, 2015;Lai et al, 2011); (ii) shape-based features may capture the particular characteristics to recognise cells or some pathologies -tumours or cancer -, some results suggest that these shapes are not accurate diagnostic features but it can be used for specific aims (Kothari et al, 2013;Melnyk, 2015;Sharma et al, 2012); (iii) colour representation may be used for diagnosis diseases and as an sign to recognise cells or tissues. However, the colour of histological images may vary according to the stain, the preparation procedure 2.…”
Section: Feature Selection and Machine Learning Algorithms Applied Tomentioning
confidence: 99%