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Segmentation of images having inhomogeneous intensities is always challenging. In this paper, we propose a model based on new local data statistics using local means and variances for detection of region of interest in medical images suffered from intensity inhomogeneity. This is done by introducing a new probability density function based on coefficient of variation, which is a best measure for inhomogeneous data. The new energy functional in the proposed model is then expressed in terms of level set function and is minimized for optimal energy. Minimization of the energy will lead to a partial differential equation, which is solved by using well known explicit method. Results of the proposed model are compared with other state of the art models and found that the proposed model outperform other existing models. Comparison is given in both qualitative and quantitative way. Furthermore, the proposed model is tested on different type of medical images like MRI, CT, Mammogram and skin lesion etc. INDEX TERMS Gaussian processes, image segmentation, intensity inhomogeneity, level set method, variational techniques.

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Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently.