Ali Madooei

Automatic Detection of Blue-White Veil by Discrete Colour Matching in Dermoscopy Images

Skin lesions are often comprised of various colours. The presence of multiple colours with an irregular distribution can signal malignancy. Among common colours under dermoscopy, blue-grey (blue-white veil) is a strong indicator of malignant melanoma. Since it is not always easy to visually identify and recognize this feature, a computerised automatic colour analysis method can provide the clinician with an objective second opinion. In this paper, we put forward an innovative method, through colour analysis and computer vision techniques, to automatically detect and segment blue-white veil areas in dermoscopy images. The proposed method is an attempt to mimic the human perception of lesion colours, and improves and outperforms the state-of-the-art as shown in our experiments.

Intrinsic melanin and hemoglobin colour components for skin lesion malignancy detection

In this paper we propose a new log-chromaticity 2-D colour space, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: (i) the effects of the particular camera characteristics for the camera system used in forming RGB images; (ii) the colour of the light used in the dermoscope; (iii) shading induced by imaging non-flat skin surfaces; (iv) and light intensity, removing the effect of light-intensity falloff toward the edges of the dermoscopic image. In the context of a blind source separation of the underlying colour, we arrive at intrinsic melanin and hemoglobin images, whose properties are then used in supervised learning to achieve excellent malignant vs. benign skin lesion classification. In addition, we propose using the geometric-mean of colour for skin lesion segmentation based on simple grey-level thresholding, with results outperforming the state of the art.

Detecting specular highlights in dermatological images

There is an increasing interest in computer-aided diagnosis of skin cancers through analysis of dermatological images. This is processing that attempts to correlate diagnosis with skin lesion appearance (i.e., the image), by extracting visual features such as colour, pigmentation, size, etc. Presence of glare (specular highlights) can confuse such systems; highlights may obscure skin surface details and appear as additional features that are not intrinsic to the lesion. In this paper, we put forward a simple method to detect specular highlights specific to dermatological images. Knowledge of the location of specularities is advantageous since it allows us to then deal with them, either by excluding them from further processing or by attempting to recover the image data in specular regions. The proposed method is built on the dichromatic reflection model and, in a novel step, uses non-negative matrix factorization with sparseness constraints to separate the specular component.

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, 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.

A Probabilistic Approach to Quantification of Melanin and Hemoglobin Content in Dermoscopy Images

We describe 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 analysis of skin hyperpigmentation, and for classification of skin diseases. The proposed method outperforms existing approaches while allowing for more stringent and probabilistic modeling than previously.

Hyperspectral Imaging and Classification for Grading Skin Erythema

Erythema 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, color imaging, and reflectance spectroscopy. 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.

Hyperspectral Image Processing for Detection and Grading of Skin Erythema

Visual 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.