Fractal analysis is currently in full swing in particular in the medical field because of the fractal nature of natural phenomena (vascular system, nervous system, bones, breast tissue ...). For this, many algorithms for estimating the fractal dimension have emerged. Most of them are based on the principle of box counting. In this work we propose a new method for calculating fractal attributes based on contrast homogeneity and energy that have been extracted from gray level co-occurrence matrix. As application we are investigated in the characterization and classification of mammographic images with SuportVectorMachine classifier. We considered in particular images with tumor masses and architectural disorder to compare with normal ones. We calculate, for comparison the fractal dimension obtained by a reference method (triangular prism) and perform a classification similar to the previous. Results obtained with new algorithm are better than reference method (classification rate is 0.91 vs 0.65). Hence new fractal attributes are relevant. 

As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including color medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for color medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested color images. Our algorithm provides very important PSNR and MSSIM values for color medical images.

Organ segmentation is an important step in various medical image applications. Accurate spleen segmentation in abdominal CT images is one of the most important steps for computer aided spleen pathology diagnosis. In this paper, we have proposed a new semi-automatic algorithm for spleen area extraction in abdominal CT images. The algorithm contains several stages. A spleen segmentation method is based on watershed approach. The first, we seek to determine the region of interest by applying the morphological filters such as the geodesic reconstruction to extract the spleen. Secondly, a pre-processing method is employed. In this step, we propose a method for improving the image gradient by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the spleen segmentation by the watershed transform controlled by markers. The new segmentation technique has been evaluated on different CT images, by comparing the semi-automatically detected spleen contour to the spleen boundaries manually traced by an expert. The experimental results are described in the last part in this work. The automated method provides a sensitivity of 95% with specificity of 99% and performs better than other related methods.

Diabetic retinopathy is a severe and widely spread eye disease. Early diagnosis and timely treatment of these clinical signs such as hard exudates could efficiently prevent blindness. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with high sensitivity. In this paper, we combine the k-means clustering algorithm and mathematical morphology to detect hard exudates (HEs) in retinal images of several diabetic patients. This method is tested on a set of 50 ophthalmologic images with variable brightness, color, and forms of HEs. The algorithm obtained a sensitivity of 95.92%, predictive value of 92.28% and accuracy of 99.70% using a lesion-based criterion.