%0 Journal Article
%T Mathematical morphology-based approach to the enhancement of morphological features in medical images
%A Yoshitaka Kimori
%J Journal of Clinical Bioinformatics
%D 2011
%I BioMed Central
%R 10.1186/2043-9113-1-33
%X The proposed method involves two steps: (1) selective extraction of target features by mathematical morphology and (2) enhancement of the extracted features by two contrast modification techniques.The goal of the proposed method is to enable enhancement of fine morphological features of a lesion region with high suppression of surrounding tissues. The effectiveness of the method was evaluated in quantitative terms of the contrast improvement ratio. The results clearly show that the method outperforms five conventional contrast enhancement methods. The effectiveness and usefulness of the proposed method were further demonstrated by application to three types of medical images: a mammographic image, a chest radiographic image, and a retinal image.The proposed method enables specific extraction and enhancement of mass lesions, which is essential for clinical diagnosis based on medical image analysis. Thus, the method can be expected to achieve automatic recognition of lesion location and quantitative analysis of legion morphology.In contemporary medical practices, image-based diagnosis is a crucial component of disease evaluation. Medical images of various modalities such as X-ray, mammography, computed tomography, magnetic resonance imaging, color fundus imaging, and ultrasound contain important information for clinical diagnosis.Computer-aided detection (CADe) and/or diagnosis (CADx) [1-4] is used to assist physicians in interpreting image-based information accurately and efficiently. CADe is the system of identifying the location of potential lesions within a medical image. CADx is the system of evaluating or characterizing the lesions, which were initially located by CADe. In general, both systems are referred to collectively as CAD. It includes the following fundamental components: image processing, image segmentation, classification, registration, modeling, visualization, etc. The schematic diagram of typical CAD system is shown in Figure 1.Of these, image proces
%K Mathematical morphology
%K Contrast enhancement
%K Mammographic image
%K Chest radiographic image
%K Retinal image
%U http://www.jclinbioinformatics.com/content/1/1/33