Automatic Image Characterization of Psoriasis Lesions

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.

AUTOMATIC NIPPLE DETECTION IN MAMMOGRAMS USING LOCAL MAXIMUM FEATURES ALONG BREAST CONTOUR

Mammogram registration is an important preprocessing technique, which helps in finding asymmetrical regions in left and right breast. However, correct nipple position is the crucial key point of mammogram registration since it is the only consistent and stable landmark upon a mammogram. To locate the nipple coordinates accurately in mammogram images, this work improves previous algorithms such as maximum height of the breast border (MHBB) and proposes a novel method consisting of local spatial-maximum mean intensity (LSMMI), local maximum zero-crossing (LMZC) based on the second-order derivative, and a combined approach dependent on LSMMI and LMZC. The proposed method is tested on 413 mammogram images from MIAS and DDSM databases. Consequently, the mean Euclidean distance (MED) between the ground truth identified by the radiologist and the detected nipple position is 0.64 cm, within 1 cm of the gold standard, for estimating the proposed method. The experimental results hence indicate that our proposed method can detect the nipple positions more accurately than other previous methods. Furthermore, the proposed select visible-nipple mammograms (SVNM) algorithm with the ability of generalization has achieved a 99% selection rate for automatic clustering of nipples in a mammography database, besides automatically detecting the breast border and nipple positions in mammograms.