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In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.<\/jats:p>","DOI":"10.3233\/kes-200040","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T17:56:18Z","timestamp":1601661378000},"page":"183-193","source":"Crossref","is-referenced-by-count":0,"title":["Minimum square distance thresholding method applying gray-gradient co-occurrence matrix"],"prefix":"10.1177","volume":"24","author":[{"given":"Hong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Qiang","family":"Zhi","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/KES-200040_ref1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.eswa.2019.01.031","article-title":"Interactive image segmentation using label propagation through complex networks","volume":"123","author":"Breve","year":"2019","journal-title":"Expert Systems With Applications"},{"key":"10.3233\/KES-200040_ref2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.media.2016.01.005","article-title":"A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI","volume":"30","author":"Avendiab","year":"2016","journal-title":"Medical Image Analysis"},{"key":"10.3233\/KES-200040_ref3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.patrec.2015.12.004","article-title":"Learning automata for image segmentation","volume":"74","author":"Sang","year":"2016","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/KES-200040_ref4","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.compag.2018.09.034","article-title":"Image segmentation for whole tomato plant recognition at night","volume":"154","author":"Xiang","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"issue":"1","key":"10.3233\/KES-200040_ref5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1117\/1.1631315","article-title":"Survey over image thresholding techniques and quantitative performance evaluation","volume":"13","author":"Sezgin","year":"2004","journal-title":"Journal of Electronic Imaging"},{"key":"10.3233\/KES-200040_ref6","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1016\/j.patcog.2014.10.020","article-title":"Co-occurrence probability-based pixel pairs background model for robust objects detection in dynamic scenes","volume":"48","author":"Liang","year":"2015","journal-title":"Pattern Recognition"},{"issue":"2","key":"10.3233\/KES-200040_ref7","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/0167-8655(85)90004-2","article-title":"On image enhancement and threshold selection using the gray-level co-occurrence matrix","volume":"3","author":"Chanda","year":"1985","journal-title":"Pattern recognition Letters"},{"key":"10.3233\/KES-200040_ref8","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.imavis.2009.10.010","article-title":"Gradient histogram: Thresholding in a region of interest for edge detection","author":"Sen","year":"2010","journal-title":"Image and Vision Computing"},{"issue":"6","key":"10.3233\/KES-200040_ref9","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TPAMI.2009.99","article-title":"Design and evaluation of more accurate gradient operators on hexagonal lattices","volume":"32","author":"Shima","year":"2010","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"crossref","unstructured":"M. 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