Extraction of features from the biomedical image using the texture and color space based image processing analysis algorithm is developed using hybrid of DWT, entropy filtering and watershed transform is discussed in this article. To extract the textures we have used entropy features using function on the MATLAB algorithm where it corresponds to the input image parameter with the use of spatial based parameters. The texture analysis based skin texture extraction algorithm consists of steps related to decomposing the input image into a set of binary images from which the color space dimensions of the resulting regions can be computed in order to describe segmented texture patterns.

Image segmentation, Image texture analysis, Image watershed transform, Image dwt2

[1] Rafael C. Gonzalez, Richard E. Woods, �Digital Image Processing�, 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.
[2] W. X. Kang, Q. Q. Yang, R. R. Liang ,�The Comparative Research on Image Segmentation Algorithms�, IEEE Conference on ETCS, pp. 703-707, 2009
[3] Ankur Mudgal and Rajdeep Singh, "A Hybrid Method of Medical Image De-nosing Using Subtraction Transform and Radial Biases Neural Network", International Journal of Computer Sciences and Engineering, Volume-03, Issue-09, Page No (54-59), Sep -2015
[4] Kamalakshi N, H Naganna and M N Shanmukhaswamy , "Modification of BM3D Algorithm for Representing Volumetric Data on Medical Images", International Journal of Computer Sciences and Engineering, Volume-01, Issue-04, Page No (11-17), Dec -2013
[5] W. X. Kang, Q. Q. Yang, R. R. Liang ,�The Comparative Research on Image Segmentation Algorithms�, IEEE Conference on ETCS, pp. 703-707, 2009.
[6] Rohlfing T, Brandt R, Menzel R, Russakoff DB,Maurer CR Jr (2005) Quo vadis, atlas-based segmentation? In: Suri JS, Wilson DL, Laxminarayan S (eds) Handbook of Biomedical Image Analysis, vol III: Registration Models. Kluwer Academic/Plenum Publishers, New York, pp 435�486, chapter 11
[7] Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, van Ginneken B (2009) Multiatlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000�1010.
[8] Stancanello J, Romanelli P, Modugno N, Cerveri P, Ferrigno G, Uggeri F, Cantore G (2006) Atlas-based identification of targets for functional radiosurgery. Med Phys 33(6):1603�1611.
[9] Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM (2003) Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad Radiol 10(3):255�265
[10] Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483�492
[11] Leemput KV (2009) Encoding probabilistic brain atlases using Bayesian inference. IEEE Trans Med Imaging 28(6):822�837.
[12] Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226�239
[13] Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C (2009) Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging 28(8):1266�1277
[14] Rohlfing T, Russakoff DB, Maurer CR (2004) Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23(8):983�994.
[15] Rohlfing T, Maurer CR Jr (2007) Shape-based averaging. IEEE Trans Image Process 16(1): 153�161