Automated ROI detection for histological image using fuzzy c-means and K-means algorithm
作者： Rupesh Mandal , Mousumi Gupta , Chinmoy Kar
DOI: 10.1109/ICEEOT.2016.7754869
关键词: Computer science 、 Cluster analysis 、 Color space 、 Pattern recognition 、 Segmentation-based object categorization 、 Feature extraction 、 Scale-space segmentation 、 Artificial intelligence 、 Region of interest 、 Image segmentation 、 k-means clustering 、 Computer vision
摘要: This paper deals with the automatic detection of region interest (ROI) in Histopathologoical images by means advanced segmentation techniques. The clustering techniques like fuzzy C-means (FCM) and K-means algorithms for color image are implemented on a dataset which consisted skin images. Euclidian distance is used as metric both algorithms. resultant segmented regions were obtained from these two compared found to be similar features. entire experiment L∗a∗b∗ space concise discussion regarding conversion carried out. presents detailed various steps its requirements shows how result them could diagnosis purpose human experts medical treatment.
sci-hub.se 参考文章(20)
Louis Wehenkel, Pierre Geurts, Raphaël Marée, Marie Dumont, FAST MULTI-CLASS IMAGE ANNOTATION WITH RANDOM SUBWINDOWS AND MULTIPLE OUTPUT RANDOMIZED TREES international conference on computer vision theory and applications. ,vol. 2, pp. 196- 203 ,(2009)
Aranzazu Jurio, Miguel Pagola, Mikel Galar, Carlos Lopez-Molina, Daniel Paternain, A Comparison Study of Different Color Spaces in Clustering Based Image Segmentation international conference information processing. pp. 532- 541 ,(2010) , 10.1007/978-3-642-14058-7_55
Yassine Aribi, Ali Wali, Mohamed Chakroun, Adel M. Alimi, Automatic Definition of Regions of Interest on Renal Scintigraphic Images AASRI Procedia. ,vol. 4, pp. 37- 42 ,(2013) , 10.1016/J.AASRI.2013.10.007
Sushmita Mitra, B. Uma Shankar, Medical image analysis for cancer management in natural computing framework Information Sciences. ,vol. 306, pp. 111- 131 ,(2015) , 10.1016/J.INS.2015.02.015
Nikhil R Pal, Sankar K Pal, A review on image segmentation techniques Pattern Recognition. ,vol. 26, pp. 1277- 1294 ,(1993) , 10.1016/0031-3203(93)90135-J
Joseph C Dunn, None, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters Journal of Cybernetics. ,vol. 3, pp. 32- 57 ,(1973) , 10.1080/01969727308546046
A. K. Jain, M. N. Murty, P. J. Flynn, Data clustering: a review ACM Computing Surveys. ,vol. 31, pp. 264- 323 ,(1999) , 10.1145/331499.331504
Chao-Hui Huang, Antoine Veillard, Ludovic Roux, Nicolas Loménie, Daniel Racoceanu, Time-efficient sparse analysis of histopathological whole slide images Computerized Medical Imaging and Graphics. ,vol. 35, pp. 579- 591 ,(2011) , 10.1016/J.COMPMEDIMAG.2010.11.009
Samuel Pak, Andrew Taylor, Ernest V. Garcia, Russell Folks, Totally automatic definition of renal regions of interest from 99mTc-MAG3 renograms: validation in patients with normal kidneys and in patients with suspected renal obstruction. Nuclear Medicine Communications. ,vol. 31, pp. 366- 374 ,(2010) , 10.1097/MNM.0B013E3283362AA3
Richard S. Hunter, Photoelectric Color Difference Meter Journal of the Optical Society of America. ,vol. 48, pp. 985- 995 ,(1958) , 10.1364/JOSA.48.000985