A CAD System Framework for the Automatic Diagnosis and Annotation of Histological and Bone Marrow Images

Due to ever increasing of medical images data in the world’s medical centers and recent developments in hardware and technology of medical imaging, necessity of medical data software analysis is needed. Equipping medical science with intelligent tools in diagnosis and treatment of illnesses has resulted in reduction of physicians’ errors and physical and financial damages. In this article we propose a computer – aided diagnosis system framework in order to automatic classification and annotation of histological and bone marrow images. The proposed method has been tested on two data set including cytological and histological images. Images context features are used to train support vector machine classifier and the accuracy of classifier is 96%. Results show that the proposed framework can be a software model in order to classify and annotate microscopic images in clinical routine functions.

[1]  A. Glaros,et al.  Understanding the accuracy of tests with cutting scores: the sensitivity, specificity, and predictive value model. , 1988, Journal of clinical psychology.

[2]  Abdul Rahman Ramli,et al.  A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing , 2009, Biological Procedures Online.

[3]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[4]  Vahid Taimouri,et al.  Segmentation of cell nuclei in heterogeneous microscopy images: A reshapable templates approach , 2013, Comput. Medical Imaging Graph..

[5]  Hassan Ghassemian,et al.  Using GLCM and Gabor filters for classification of PAN images , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[6]  Atam P. Dhawan,et al.  Classification of melanoma using tree structured wavelet transforms , 2003, Comput. Methods Programs Biomed..

[7]  T.-T. Van Cao,et al.  A CFAR thresholding approach based on test cell statistics , 2004 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[10]  Sukhendu Das,et al.  Unsupervised texture segmentation using feature selection and fusion , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Vassili A. Kovalev,et al.  Robust recognition of white blood cell images , 1996, Proceedings of 13th International Conference on Pattern Recognition.