Jorge S. Marques

Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

Performance evaluation of object detection algorithms for video surveillance

In this paper, we propose novel methods to evaluate the performance of object detection algorithms in video sequences. This procedure allows us to highlight characteristics (e.g., region splitting or merging) which are specific of the method being used. The proposed framework compares the output of the algorithm with the ground truth and measures the differences according to objective metrics. In this way it is possible to perform a fair comparison among different methods, evaluating their strengths and weaknesses and allowing the user to perform a reliable choice of the best method for a specific application. We apply this methodology to segmentation algorithms recently proposed and describe their performance. These methods were evaluated in order to assess how well they can detect moving regions in an outdoor scene in fixed-camera situations

PH 2 - A dermoscopic image database for research and benchmarking

The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair evaluation of multiple systems. In this paper, a dermoscopic image database, called PH2, is presented. The PH2 database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images. The PH2 database will be made freely available for research and benchmarking purposes.

A class of constrained clustering algorithms for object boundary extraction

Boundary extraction is a key task in many image analysis operations. This paper describes a class of constrained clustering algorithms for object boundary extraction that includes several well-known algorithms proposed in different fields (deformable models, constrained clustering, data ordering, and traveling salesman problems). The algorithms belonging to this class are obtained by the minimization of a cost function with two terms: a quadratic regularization term and an image-dependent term defined by a set of weighting functions. The minimization of the cost function is achieved by lowpass filtering the previous model shape and by attracting the model units toward the centroids of their attraction regions. To define a new algorithm belonging to this class, the user has to specify a regularization matrix and a set of weighting functions that control the attraction of the model units toward the data. The usefulness of this approach is twofold: it provides a unified framework for many existing algorithms in pattern recognition and deformable models, and allows the design of new recursive schemes.

Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features

Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the development of diagnosis methods for this disease. This paper addresses two different systems for the detection of melanomas in dermoscopy images. The first system uses global methods to classify skin lesions, whereas the second system uses local features and the bag-of-features classifier. This paper aims at determining the best system for skin lesion classification. The other objective is to compare the role of color and texture features in lesion classification and determine which set of features is more discriminative. It is concluded that color features outperform texture features when used alone and that both methods achieve very good results, i.e., Sensitivity = 96% and Specificity = 80% for global methods against Sensitivity = 100% and Specificity = 75% for local methods. The classification results were obtained on a data set of 176 dermoscopy images from Hospital Pedro Hispano, Matosinhos.

Crater Detection by a Boosting Approach

An approach to automatically detect impact craters on planetary surfaces is presented in this letter. It is built up from a boosting algorithm proposed by Viola and Jones (2004) whose simplicity combined with an original learning strategy leads to a fast and robust process with consistent results. The approach is validated with image data sets from Mars surface captured by the Mars Orbiter Camera onboard Mars Global Surveyor probe.

Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation

Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the control theory.

Trajectory Classification Using Switched Dynamical Hidden Markov Models

This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical hidden Markov model (SD-HMM). The performance of the proposed method is illustrated with real data from two different scenarios: a shopping center and a university campus. A set of human activities in both scenarios is successfully recognized by the proposed system. These experiments show the ability of our approach to properly describe trajectories with sudden changes.

A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters

A pigment network is one of the most important dermoscopic structures. This paper describes an automatic system that performs its detection in dermoscopy images. The proposed system involves a set of sequential steps. First, a preprocessing algorithm is applied to the dermoscopy image. Then, a bank of directional filters and a connected component analysis are used in order to detect the “lines” of the pigment network. Finally, features are extracted from the detected network and used to train an AdaBoost algorithm to classify each lesion regarding the presence of the pigment network. The algorithm was tested on a dataset of 200 medically annotated images from the database of Hospital Pedro Hispano (Matosinhos), achieving a sensitivity = 91.1% and a specificity = 82.1%.

Improved pitch prediction with fractional delays in CELP coding

A scheme is discussed for long-term prediction in CELP (code-excited linear predictive) coding using fractional delay prediction. This technique permits a more accurate representation of voiced speech and achieves an improvement of synthetic quality for female speakers. The higher complexity of this type of predictor relative to the classical one is its major disadvantage. Suboptimal schemes in which the search for the functional pitch delay is restricted to a neighborhood of an integer pitch estimate can be envisaged to decrease the computational load.<<ETX>>