Aurora Sáez

Model-Based Classification Methods of Global Patterns in Dermoscopic Images

In this paper different model-based methods of classification of global patterns in dermoscopic images are proposed. Global patterns identification is included in the pattern analysis framework, the melanoma diagnosis method most used among dermatologists. The modeling is performed in two senses: first a dermoscopic image is modeled by a finite symmetric conditional Markov model applied to L*a*b* color space and the estimated parameters of this model are treated as features. In turn, the distribution of these features are supposed that follow different models along a lesion: a Gaussian model, a Gaussian mixture model, and a bag-of-features histogram model. For each case, the classification is carried out by an image retrieval approach with different distance metrics. The main objective is to classify a whole pigmented lesion into three possible patterns: globular, homogeneous, and reticular. An extensive evaluation of the performance of each method has been carried out on an image database extracted from a public Atlas of Dermoscopy. The best classification success rate is achieved by the Gaussian mixture model-based method with a 78.44% success rate in average. In a further evaluation the multicomponent pattern is analyzed obtaining a 72.91% success rate.

Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis

BackgroundThe diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract useful information from muscle biopsies, developing a novel method that analyzes muscle samples in an objective, automated, fast and precise manner.MethodsOur database consisted of 102 muscle biopsy images from 70 individuals (including controls, patients with neurogenic atrophies and patients with muscular dystrophies). We used this to develop a new method, Neuromuscular DIseases Computerized Image Analysis (NDICIA), that uses network science analysis to capture the defining signature of muscle biopsy images. NDICIA characterizes muscle tissues by representing each image as a network, with fibers serving as nodes and fiber contacts as links.ResultsAfter a ‘training’ phase with control and pathological biopsies, NDICIA was able to quantify the degree of pathology of each sample. We validated our method by comparing NDICIA quantification of the severity of muscular dystrophies with a pathologist’s evaluation of the degree of pathology, resulting in a strong correlation (R = 0.900, P <0.00001). Importantly, our approach can be used to quantify new images without the need for prior ‘training’. Therefore, we show that network science analysis captures the useful information contained in muscle biopsies, helping the diagnosis of muscular dystrophies and neurogenic atrophies.ConclusionsOur novel network analysis approach will serve as a valuable tool for assessing the etiology of muscular dystrophies or neurogenic atrophies, and has the potential to quantify treatment outcomes in preclinical and clinical trials.

Pattern Analysis in Dermoscopic Images

In this chapter an extensive review of algorithmic methods that automatically detect patterns in dermoscopic images of pigmented lesions is presented. Pattern Analysis seeks to identify specific patterns, which may be global and local. It is the method most commonly used for providing diagnostic accuracy for cutaneous melanoma. In this chapter, a description of global and local patterns identified by pattern analysis is presented as well as a brief explanation of algorithmic methods that carry out the detection and classification of these patterns. Although the 7-Point Checklist method corresponds to a different diagnostic technique than pattern analysis, it can be considered as a simplification of it as it classifies seven features related with local patterns. For this reason, the main techniques to automatically assess the 7-Point Checklist are briefly explained in this chapter.

Neuromuscular disease classification system

Abstract. Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

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.

Normalized Cut optimization based on color perception findings. A comparative study

This paper proposes a methodology to obtain a fully automatic color segmentation algorithm based on the Normalized Cut (Ncut) proposed by Shi and Malik, using recent findings in color perception. A weighting matrix computed using a perceptually uniform color space (CIE

Tackling the ordinal and imbalance nature of a melanoma image classification problem

L^*a^*b^*

Classification of Melanoma Presence and Thickness Based on Computational Image Analysis

L∗a∗b∗) and color distance formulae correlated with the visually perceived color differences (CIE94 and CIEDE2000); a stopping condition related to perceptual criteria; an automatic parameters setting required to compute the affinity matrix are proposed. To test the proposed methodology, a wide study about the influence of the color space choice, different stopping conditions, and different similarity measurements is carried out. These alternatives are exhaustively evaluated using perception-related measurements (S-CIELAB) and general segmentation evaluation metrics applied to the 500 images of the Berkeley database. The results showed that the proposed method outperforms Ncut based on other color spaces, similarity measure or stopping conditions. Furthermore, the usability of the method is increased by replacing the manual parameter setting for an automatic.