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

Multi-spectral light interaction modeling and imaging of skin lesions

Nevoscope as a diagnostic tool for melanoma was evaluated using a white light source with promising results. Information about the lesion depth and its structure will further improve the sensitivity and specificity of melanoma diagnosis. Wavelength-dependent variable penetration power of monochromatic light in the trans-illumination imaging using the Nevoscope can be used to obtain this information. Optimal selection of wavelengths for multi-spectral imaging requires light-tissue interaction modeling. For this, three-dimensional wavelength dependent voxel-based models of skin lesions with different depths are proposed. A Monte Carlo simulation algorithm (MCSVL) is developed in MATLAB and the tissue models are simulated using the Nevoscope optical geometry. 350-700nm optical wavelengths with an interval of 5nm are used in the study. A correlation analysis between the lesion depth and the diffuse reflectance is then used to obtain wavelengths that will produce diffuse reflectance suitable for imaging and give information related to the nevus depth and structure. Using the selected wavelengths, multi-spectral trans-illumination images of the skin lesions are collected and analyzed. An adaptive wavelet transform based tree-structure classification method (ADWAT) is proposed to classify epi-illuminance images of the skin lesions obtained using a white light source into melanoma and dysplastic nevus images classes. In this method, tree-structure models of melanoma and dysplastic nevus are developed and semantically compared with the tree-structure of the unknown image for classification. Development of the tree-structure is dependent on threshold selections obtained from a statistical analysis of the feature set. This makes the classification method adaptive. The true positive value obtained for this classifier is 90% with a false positive of 10%. The Extended ADWAT method and Fuzzy Membership Functions method using combined features from the epi-illuminance and multi-spectral images further improve the sensitivity and specificity of melanoma diagnosis. The combined feature set with the Extended-ADWAT method gives a true positive of 93.33% with a false positive of 8.88%. The Gaussian Membership Functions give a true positive of 100% with a false positive of 17.77% while the Bell Membership Functions give a true positive of 100% with a false positive of 4.44%

Improving skin cancer (melanoma) detection : new method

University of Technology Sydney. Faculty of Engineering and Information Technology.Melanoma, the deadliest form of skin cancer, must be diagnosed early for effective treatment. Rough pigment network and qualities are important signs for melanoma diagnosis using pathologist images. The main focus of this thesis is to improve skin cancer (Melanoma) detection through introducing novel image processing approach for a computer-aided system based on pigment network and elements detection on pathology images. It is important to propose an automated system for differentiating between melanocytic nevi and malignant melanoma. This thesis describes a novel image processing approach for computer-aided pigment network and elements detection on dermoscopy / pathology images. The proposed methods provide meaningful ideas of structures, and extract features for melanoma detection. Additionally, the thesis presents efforts towards prevention of melanoma, by developing a smart system to locate pigment networks. The thesis aims to cover a complete theoretical model for simulating the processes that takes place when a human interprets an image generated by the eye, through designing a reliable system, that can provide a screening method that “filters” lesions and melanoma in a general practice. The proposed system is to be used with a standard PC with input from a high quality digital camera, dermoscopy / microscopy slides or any other suitable hardware sources. This system analyses the structure of a mole / skin defects, detects cancer, identifies features, makes a decision and provides the result. The result of the proposed system shows that the Skin Cancer (Melanoma) Detection strategy which uses SVM performs reasonably satisfactorily (accuracy 77.44%, sensitivity 83.60 %, and specify 70.67%). Furthermore, the SVM based wavelet Gabor (SVM-WLG) performs better than the SVM (81.61%, 88.48%, and 74.51 % accuracy, sensitivity, and specify respectively). However, the Swarm-based SVM (SSVM) performs better than the other two algorithms, with average for accuracy, sensitivity, specificity of 87.13%, 94.1% and 80.22%, respectively

Computational Diagnosis of Skin Lesions from Dermoscopic Images using Combined Features

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm

Review on automatic early skin cancer detection

Skin cancer is increasing in different countries especially in Australia. Early detection of skin cancer can treat melanoma successfully, therefore, curability and survival depends directly on removing melanoma in its early stages. Since clinical observations face to different fault for melanoma detection, the automatic diagnosis can help to increase the accuracy of detection. Reviewing the researches have done in skin cancer detection and providing the overview on automatic detection of skin cancer are the ultimate aims of this paper. It presents the literature on automatic skin cancer detection and describes the different steps of such process. © 2011 IEEE

The Manifold Scattering Transform for High-Dimensional Point Cloud Data

The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model focused primarily on its theoretical stability and invariance properties but did not provide methods for its numerical implementation except in the case of two-dimensional surfaces with predefined meshes. In this work, we present practical schemes, based on the theory of diffusion maps, for implementing the manifold scattering transform to datasets arising in naturalistic systems, such as single cell genetics, where the data is a high-dimensional point cloud modeled as lying on a low-dimensional manifold. We show that our methods are effective for signal classification and manifold classification tasks.Comment: Accepted for publication in the TAG in DS Workshop at ICML. For subsequent theoretical guarantees, please see Section 6 of arXiv:2208.0856

The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture.