The X-ray images are extensively used by the medical practitioners to detect the minute fractures of bone images as they are painless and economical compared to other image modalities. The minute fractures cannot be identified with nacked eye. So the X-ray images are to be processed for detecting the minute fractures. The orthogonal wavelet transforms like Haar, daubechies etc can be used as edge detector, but a lot of false edge information will be extracted. Edge detection of X-ray images using Multiresolution Analysis(MRA) based biorthogonal wavelets is more preferable when compared with orthogonal wavelets because of more flexibility. Therefore biorthogonal wavelet transforms like bior1.3,bior2.4 are applied to detect the edges and are compared for edge feature extraction. Among all the methods, biorthogonal wavelet bior1.3 performs well in detecting the edges with better quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS), peak signal to noise ratio (PSNR) and computation time are compared for various wavelets for edge detection

This paper presents a listless variant of a modified three-dimensional (3D)-block coding algorithm suitable for medical image compression. A higher degree of correlation is achieved by using a 3D hybrid transform. The 3D hybrid transform is performed by a wavelet transform in the spatial dimension and a Karhunen–Loueve transform in the spectral dimension. The 3D transformed coefficients are arranged in a one-dimensional (1D) fashion, as in the hierarchical nature of the wavelet-coefficient distribution strategy. A novel listless block coding algorithm is applied to the mapped 1D coefficients which encode in an ordered-bit-plane fashion. The algorithm originates from the most significant bit plane and terminates at the least significant bit plane to generate an embedded bit stream, as in 3D-SPIHT. The proposed algorithm is called 3D hierarchical listless block (3D-HLCK), which exhibits better compression performance than that exhibited by 3D-SPIHT. Further, it is highly competitive with some of the state-of-the-art 3D wavelet coders for a wide range of bit rates for magnetic resonance, digital imaging and communication in medicine and angiogram images. 3D-HLCK provides rate and resolution scalability similar to those provided by 3D-SPIHT and 3D-SPECK. In addition, a significant memory reduction is achieved owing to the listless nature of 3D-HLCK.

In these modern times, due to air pollution human skin is affected in many ways and these have become the most prevalent diseases worldwide. It may cause diseases like Actinic keratosis, Melanoma, Psoriasis, Eczema Skin disease is the fourth leading cause of non-toxic disease globally, affecting 7.9–60% of individuals in all age groups. The advancement of medical technology has led easier ways in identification of these diseases but the cost of such diagnosis is very expensive and limited. So, Image processing techniques guide to build computerized screening systems at initial stage. In this research work, we have come with a method to identify and classify the diseases. The implementation of the project consists of a dual stage approach to detect skin diseases based on their classification and severity. The second stage involves the training and testing of images using Naive- Bayes classifier to identify the skin disease.

Edge feature extraction of X-ray images plays an important role in detecting the fractures in bones. It deals with the detection of discontinuities in the gray level values which helps the medical practisioners to extract the edges of an images to detect the minute fractures of an X-ray bone image. Edge feature extraction of X-ray images using Multiresolution Analyis (MRA) based on biorthogonal wavelet is more preferable when compared with orthogonal wavelets because of more flexibility and less computation time. The orthogonal wavelet transforms like Haar, daubechies, coiflet, symlets can be used as edge detector, but a lot of false edge information will be extracted. Therefore biorthogonal wavelet transforms like bior1.3, bior2.4, bior3.9, bior5.5, bior6.8 are applied to detect the edges and are compared. Among all the methods, bior1.3 performs well in detecting the edges with better quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS), peak signal to noise ratio (PSNR) and computation time are compared for various wavelets.