Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination. Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours. The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum

Automating the ABCD Rule for Melanoma Detection: A Survey

The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule

A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Quantification of prognostic parameters for assessment of diabetes-related foot ulcers and venous leg ulcers using image processing techniques

Diabetes-related Foot Ulcers (DFUs) and Venous Leg Ulcers (VLUs) are two important types of chronic wounds which do not heal in an orderly fashion and being a major cause of morbidity in extreme cases. Assessment of these ulcers is a growing concern among health care professionals across the globe. Currently, healing of these ulcers is assessed by monitoring the changes in their area over four consecutive weeks. The suggested clinical monitoring parameter is that the ulcers which show more than fifty percent reduction in the area by week four (after the ulcers are reported in the clinics) are predicted to heal within twelve weeks of time. However, this is a subjective measurement, performed manually using a ruler; based on the assumption that the ulcer is purely rectangular. Moreover, the above-mentioned monitoring method fails to work in most of the cases, as healing of these ulcers is a complicated, multifactorial process which cannot be assessed only by a single parameter (i.e., area). This research work has proposed new objective parameters for assessment and prediction of these ulcers by studying the shape of the ulcers, temperature distribution of the ulcer and on the ulcerated foot and area measurement of the ulcers using different techniques. This work has also examined the association of the proposed parameters with patient's clinical information, etiological factors and the healing status of the ulcers. Literature has suggested that there is a change in the irregularity of the ulcers as they heal, thus, playing the role in the healing of the ulcers. Based on this fact, this work hypothesized that the edges of the ulcers can be assessed by quantifying the change in irregularity in them. The widely used technique of measurement of irregularity is Fractal Dimension (FD). However, FD has the limitation of inherent limited resolution of the digitized images, which renders these images as non-fractal, as the self-similarity properties of the images are lost. Thus, this work proposed a new index measure and developed an algorithm for measurement of irregularity and tested it on synthetic images initially. The new index measure called curve irregularity index (Ic) measures the change in the irregularity of the segments of the contours with change in window sizes and does not assume the objects to have self-similar properties. The Ic was then measured and validated on the contours of DFUs and VLUs and the association of irregularity of the contours with etiological factors and the healed status of the ulcers, respectively, was reported. This work has employed the normal DSLR camera and digital planimetry technique to capture the RGB images and to obtain the tracings of the ulcers respectively. This work has shown the significance of contour irregularity of ulcers with the clinical conditions of patients and differentiated between the healed and not-healed ulcers. This research has also employed infrared thermal imaging technique to obtain the temperature distribution of the ulcers. Literature has reported the association of temperature with the risk of ulceration in neuropathic and ischemic conditions of the feet. However, very few works have been done on the study of temperature of the existing ulcers. Hence, this work tested and obtained the association of the mean temperature of the DFUs with the clinical conditions of the participants. This work also hypothesized that the area obtained based on thermal distribution can differentiate between the healed and not-healed ulcers in VLUs. Hence, segmentation of the ulcer region from the thermal images was performed based on an active contour model, previously developed for segmentation of contours in images where edges are not defined by gradient. The obtained results showed that the area thus obtained from the ulcer regions of all five weeks showed association with the healed status of the VLUs and can differentiate between the healed and not-healed ulcers. This work can also be used to predict the healing trajectory of the ulcers. Thus, the overall research work would find application in the clinical set ups to aid in the assessment and prediction of the healing status of DFUs and VLUs and would lead to provide better health-related quality of lives to the patients