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

Hyperspectral Image Processing for Detection and Grading of Skin Erythema

International audienceVisual assessment is the most common clinical investigation of skin reactions in radiotherapy. Due to the subjective nature of this method, additional noninvasive techniques are needed for more accurate evaluation. Our goal is to evaluate the effectiveness of hyperspectral image analysis for that purpose. In this pilot study, we focused on detection and grading of skin Erythema. This paper reports our proposed processing pipeline and experimental findings. Experiments have been performed to demonstrate the efficacy of the proposed approach for (1) reproducing clinical assessments, and (2) outperforming RGB imaging data

Hyperspectral Imaging and Classification for Grading Skin Erythema

International audienceErythema is an inflammatory condition of the skin that is commonly used as a feature to monitor the progression of cutaneous diseases or treatment induced side effects. In radiation therapy, skin erythema is routinely assessed visually by an expert using standardized grading criteria. However, visual assessment (VA) is subjective and commonly used grading tools are too coarse to score the onset of erythema. Therefore, an objective method capable of quantitatively grading early erythema changes may help identify patients at higher risk for developing severe radiation induced skin toxicities. The purpose of this study is to investigate the feasibility of using hyperspectral imaging (HSI) for quantitative assessment of early erythema and to characterize its performance against VA documented on conventional digital photographic red-green-blue (RGB) images. Erythema was induced artificially on 3 volunteers in a controlled pilot study; and was subsequently measured using HSI and color imaging. HSI and color imaging data was analyzed using linear discriminant analysis (LDA) to perform classification. The classification results, including accuracy, and precision, demonstrated that HSI is superior to color imaging in skin erythema assessment

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images

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, clinician 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 thesis provides a detailed exposition of my recent contributions in this research direction. In particular, it describes two novel approaches for utilizing colour both as low-level and high- level image feature. The first contribution describes a technique that employs the stochastic Latent Topic Models framework to allow quantification of melanin and hemoglobin content in dermoscopy images. Such information bears useful implications for the analysis of skin hyper-pigmentation, and for classification of skin diseases. The second contribution is a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure. This is achieved in a Multiple Instance Learning framework with only image-level labels of whether the feature is present or not. As the output, we predict the image label and also localize the feature in the image. Experiments are conducted on a challenging dataset with results outperforming state-of-the-art. Moreover, the thesis explores using physic-based photometric models to enhance dermoscopy image analysis. In particular, it proposes methods for colour-to-greyscale conversion, shading removal, and glare attenuation. The studies reported in this thesis provide an improvement on the scope of modelling for computerized image analysis of skin lesions