224 research outputs found
Aspects of skin cancer diagnosis in clinical practice
Skin cancer incidence is increasing in fair-skinned populations. The three most common skin cancers are basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). A correct diagnosis is crucial for an efficient and tailored treatment for the skin cancer patient. The purpose of this thesis was to evaluate different aspects of the preoperative diagnosis of skin cancer. The studies making up this thesis were based on analysis of data from a register including all skin tumour excisions at the Department of Dermatology in Helsingborg, Sweden, from March 2008 to January 2015. The registered data included e.g. sex and age of the patient, tumour site and size, dermoscopic features of the tumour, the preliminary preoperative and final postoperative (histopathological) diagnosis as well as tumour cells at surgical margins. The preliminary preoperative clinical diagnosis was compared with the final histopathological diagnosis in 2,953 excised tumours, whereof 1,626 (55.1%) were malignant, showing high diagnostic accuracy for the diagnosis of malignant tumour and for the diagnosis of basal cell carcinoma (BCC). A total of 96.0% of all excisions had tumour-free margins. The number needed to excise (NNE) for melanoma (the number of pigmented lesions excised to find one melanoma) was calculated for 1,717 cases of excised skin tumours (252 melanomas, 1,395 naevi and 70 seborrhoeic keratoses (SK)). The overall NNE value during the study period was 6.5 (SKs not included). When SKs were included in the calculations the NNE was 6.8. The NNE value decreased with increasing age of the patient and varied for different body locations, with the highest values found for the trunk and the lowest for the arms. When the ABCD rule of dermoscopy was used preoperatively at the bedside in 309 cases (46 melanomas and 263 naevi), use of the algorithm achieved 83% sensitivity and 45% specificity for melanoma diagnosis. A sensitivity of 74% and specificity of 91% were seen for the clinical diagnosis. A considerable percentage (19.6%) of very early melanomas were preoperatively not expected to be melanomas by the dermatologist. The prediction of histopathological subtype of BCC is important for choosing optimal treatment in BCC patients and was assessed in 1,501 cases with pre- or postoperative diagnosis of BCC. The prediction of superficial BCC (sBCC) significantly improved after an educational update on dermoscopic criteria for sBCC in cases assessed by dermoscopy. In conclusion, these studies have shown high accuracy of the preoperative diagnosis of malignant tumour and BCC. With increasing age of the patient, a higher rate of excised pigmented skin lesions was melanomas. Bedside use of the ABCD rule of dermoscopy achieved high sensitivity but low specificity for melanoma diagnosis; however, clinical information seemed to add to specificity. Prediction of sBCC was enhanced after a dermoscopy training session and when dermoscopy was mandatory
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Segmentation and lesion detection in dermoscopic images
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMalignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of the main keys that refer to melanoma or non-melanoma disease. However, determining these clinical features can be a time-consuming, subjective (even for trained clinicians) and challenging task for several reasons: lesions vary considerably in size and colour, low contrast between an affected area and the surrounding healthy skin, especially in early stages, and the presence of several elements such as hair, reflections, oils and air bubbles on almost all images. This thesis aims to provide an accurate, robust and reliable automated dermoscopy image analysis technique, to facilitate the early detection of malignant melanoma disease. In particular, four innovative methods are proposed for region segmentation and classification, including two for pigmented region segmentation, one for pigment network detection, and one for lesion classification. In terms of boundary delineation, four pre-processing operations, including Gabor filter, image sharpening, Sobel filter and image inpainting methods are integrated in the segmentation approach to delete unwanted objects (noise), and enhance the appearance of the lesion boundaries in the image. The lesion border segmentation is performed using two alternative approaches. The Fuzzy C-means and the Markov Random Field approaches detect the lesion boundary by repeating the labeling of pixels in all clusters, as a first method. Whereas, the Particle Swarm Optimization with the Markov Random Field method achieves greater accuracy for the same aim by combining them in the second method to perform a local search and reassign all image pixels to its cluster properly. With respect to the pigment network detection, the aforementioned pre-processing method is applied, in order to remove most of the hair while keeping the image information and increase the visibility of the pigment network structures. Therefore, a Gabor filter with connected component analysis are used to detect the pigment network lines, before several features are extracted and fed to the Artificial Neural Network as a classifier algorithm. In the lesion classification approach, the K-means is applied to the segmented lesion to separate it into homogeneous clusters, where important features are extracted; then, an Artificial Neural Network with Radial Basis Functions is trained by representative features to classify the given lesion as melanoma or not. The strong experimental results of the lesion border segmentation methods including Fuzzy C-means with Markov Random Field and the combination between the Particle Swarm Optimization and Markov Random Field, achieved an average accuracy of 94.00% , 94.74% respectively. Whereas, the lesion classification stage by using extracted features form pigment network structures and segmented lesions achieved an average accuracy of 90.1% , 95.97% respectively. The results for the entire experiment were obtained using a public database PH2 comprising 200 images. The results were then compared with existing methods in the literature, which have demonstrated that our proposed approach is accurate, robust, and efficient in the segmentation of the lesion boundary, in addition to its classification
Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends
Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given
Resolution invariant wavelet features of melanoma studied by SVM classifiers
This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks
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