Combining 3D run-length encoding coding and searching techniques for medical image compression

The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average

Thermal image segmentation based on density slicing of color histogram of images

Thermal imaging has been recently used as a new approach for human biometrics. The formation of thermal image is purely based on the heat distribution of an object. It brings some difficulties to image segmentation due to its over centralized intensity distribution and low intensity contrast. Our approach uses color slicing segmentation method to characterize the texture information using various color models. The experimental results of present study shows the color slicing technique can make good effect on the segmentation of tumor structure. We have found that segmenting these images can be made easier by using RGB, HSV, YCbCr color models by Color slicing method. We have used many Statistical operations (such as contrast, variation …) to evaluate our segmentation results