Skin Lesion Segmentation Ensemble with Diverse Training Strategies

This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmentation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features. An encoder-decoder segmentation CNN is employed to take advantage of each pre-trained features extractor. Experimental results reveal how multiple initialization strategies can be exploited, by means of an ensemble method, to obtain state-of-the-art skin lesion segmentation accuracy

Automatic image segmentation by dynamic region growth and multiresolution merging

Image segmentation is a fundamental task in many computer vision applications. We present a novel unsupervised color image segmentation algorithm named GSEG, which exploits the information obtained from detecting edges in color images. By using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify the image content. Elements that contain higher gradient density are included by a dynamic generation of clusters as the segmentation progresses. By quantizing the colors in the image and extracting texture information from the neighborhood entropy of each pixel, the proposed method obtains accurate models of texture that are highly effective to merge regions that belong to the same object. Experimental results for various image scenarios in comparison with state-of-the-art segmentation techniques demonstrate the performance advantages of the proposed method

Fast unsupervised multiresolution color image segmentation using adaptive gradient thresholding and progressive region growing

In this thesis, we propose a fast unsupervised multiresolution color image segmentation algorithm which takes advantage of gradient information in an adaptive and progressive framework. This gradient-based segmentation method is initialized by a vector gradient calculation on the full resolution input image in the CIE L*a*b* color space. The resultant edge map is used to adaptively generate thresholds for classifying regions of varying gradient densities at different levels of the input image pyramid, obtained through a dyadic wavelet decomposition scheme. At each level, the classification obtained by a progressively thresholded growth procedure is combined with an entropy-based texture model in a statistical merging procedure to obtain an interim segmentation. Utilizing an association of a gradient quantized confidence map and non-linear spatial filtering techniques, regions of high confidence are passed from one level to another until the full resolution segmentation is achieved. Evaluation of our results on several hundred images using the Normalized Probabilistic Rand (NPR) Index shows that our algorithm outperforms state-of the art segmentation techniques and is much more computationally efficient than its single scale counterpart, with comparable segmentation quality

Analysis of lesion border segmentation using watershed algorithm

Automatic lesion segmentation is an important part of computer-based skin cancer detection. A watershed algorithm was introduced and tested on benign and melanoma images. The average of three dermatologists\u27 manually drawn borders was compared as the benchmark. Hair removing, black border removing and vignette removing methods were introduced in preprocessing steps. A new lesion ratio estimate was added to the merging method, which was determined by the outer bounding box ratio. In postprocessing, small blob removing and border smoothing using a peninsula removing method as well as a second order B-Spline smoothing method were included. A novel threshold was developed for removing large light areas near the lesion boundary. A supervised neural network was applied to cluster results and improve the accuracy, classifying images into three clusters: proper estimate, over-estimate and under-estimate. Comparing to the manually drawn average border, an overall of 11.12% error was achieved. Future work will involve reducing peninsula-shaped noise and looking for other reliable features for the classifier --Abstract, page iii

Active Contours Based Segmentation and Lesion Periphery Analysis For Characterization of Skin Lesions in Dermoscopy Images

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH $^{2}$ dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery

Identification and Estimation of Clinical Indices Useful for the Diagnosis of Melanoma from Macroscopic Images

openMelanoma is an extremely aggressive form of skin cancer. When not promptly detected and treated, it can quickly metastasize, leading to unfavourable prognostic outcomes. Achieving early melanoma diagnosis relies heavily on accurate and thorough skin analysis, made by an expert dermatologist. To address subjective judgments and time-expensive exams, a novel screening and diagnostic method utilising photogrammetry-derived images of skin lesions has been devised. This innovative approach is based on the acquisition of macroscopic images, depicting a large portion of the patient body, and enables the creation of a three-dimensional model of the patient, allowing for the extraction of corresponding images of each individual lesion. This thesis aims to quantitatively assess the asymmetry, the irregularity of the border and the color of skin lesions through the analysis of segmented macroscopic images, contributing to the development of an automated diagnostic tool useful to the clinician for melanoma identification. The analysis was conducted on a dataset comprising images of healthy skin lesions and lesions reported as suspicious by dermatologists among which nine cases were confirmed as melanomas by biopsy. By utilizing algorithms to objectively compute asymmetry and border irregularity parameters, coupled with an in-depth analysis of color features associated with melanocytic lesions, the investigation unveiled statistically significant differences in these attributes between benign and suspicious lesions. Indeed, statistical tests confirmed distinctive distributions of these parameters between the two skin lesion populations. These findings underscore the potential of automated diagnostic tools derived from macroscopic images in effectively identifying suspicious lesions, thus contributing to early melanoma detection strategies

Machine learning methods for binary and multiclass classification of melanoma thickness From dermoscopic images

Thickness of the melanoma is the most important factor associated with survival in patients with melanoma. It is most commonly reported as a measurement of depth given in millimeters (mm) and computed by means of pathological examination after a biopsy of the suspected lesion. In order to avoid the use of an invasive method in the estimation of the thickness of melanoma before surgery, we propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. Two supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a three-class scheme (thin, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the three-class problem, a set of ordinal classification methods (considering ordering relation between the three classes) is included. For the binary case, LIPU outperforms all the other methods with an accuracy of 77.6%, while, for the second scheme, although LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all classes