Quantification of structures of skin lesion and calibration of dermoscopy images for automated melanoma diagnosis

研究成果の概要 (和文) : 我々が開発している、インターネット上の悪性黒色腫自動診断支援システムの実用化を具体的に推進するために、課題名にある2点の開発を重点的に行った。(1)定量化研究においては、皮膚科医が臨床現場で用いるABCD rule、7-point checklistの全15項目について、皮膚科医の判断と統計的有意差が見られないモデルの構築に成功した。(2)画像補正研究においては、特殊なハードウェアなどを必要とせず、画像の明度、色彩を適切なダーモスコピー画像のものに調整する手法を確立した。研究成果の概要 (英文) : I had investigated mainly following two themes for making our Internet-based melanoma screening system fit for practical use. (1) Quantification of dermoscopic structures (2) Development of automated color calibration for dermoscopy images. For both themes, I had successfully achieved the objectives set out in the proposal. (1) Succeeded to build a recognition system that has statistically no difference with expert dermatologists in recognizing a total of 15 dermoscopic structures defined in ABCD rule and 7-point checklist. (2) Achieved equivalent color calibration performance for dermoscopy images in luminance, hue and saturation without any special devices

An improved border detection in dermoscopy images for density based clustering

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. In current practice, dermatologists determine lesion area by manually drawing lesion borders. Therefore, automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. To our knowledge, in our 2010 study we achieved one of the highest accuracy rates in the automated lesion border detection field by using modified density based clustering algorithm. In the previous study, we proposed a novel method which removes redundant computations in well-known spatial density based clustering algorithm, DBSCAN; thus, in turn it speeds up clustering process considerably.</p> <p>Findings</p> <p>Our previous study was heavily dependent on the pre-processing step which creates a binary image from original image. In this study, we embed a new distance measure to the existing algorithm. This provides twofold benefits. First, since new approach removes pre-processing step, it directly works on color images instead of binary ones. Thus, very important color information is not lost. Second, accuracy of delineated lesion borders is improved on 75% of 100 dermoscopy image dataset.</p> <p>Conclusion</p> <p>Previous and improved methods are tested within the same dermoscopy dataset along with the same set of dermatologist drawn ground truth images. Results revealed that the improved method directly works on color images without any pre-processing and generates more accurate results than existing method.</p

A soft kinetic data structure for lesion border detection

Motivation: The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach—graph spanner—for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction