Monte Carlo despeckling of transrectal ultrasound images of the prostate

In this paper, a novel stochastic method is developed for despeckling transrectal ultrasound (TRUS) images of the prostate. By incorporating the circular probe acquisition particularities and speckle noise statistics of TRUS images of the prostate into a likelihood-weighted Monte Carlo estimation scheme, the proposed method can better remove speckle noise while preserving image structures and details that are relevant for image screening, allowing for a better delineation of the lesion contour. Our in silico and in vivo experimental results are promising, which was confirmed by a clinical evaluation of the in vivo test cases by experienced clinicians, and indicate that our method potentially can perform better than other previously proposed methods.     

Fovea center detection based on the retina anatomy and mathematical morphology

In this work, we present a new fovea center detection method for color eye fundus images. This method is based on known anatomical constraints on the relative locations of retina structures, and mathematical morphology. The detection of this anatomical feature is a prerequisite for the computer aided diagnosis of several retinal diseases, such as Diabetic Macular Edema. The proposed method is adaptive to local illumination changes, and it is robust to local disturbances introduced by pathologies in digital color eye fundus images (e.g. exudates). Our experimental results using the DRIVE image database indicate that our method is able to detect the fovea center in 37 out of 37 images (i.e. with a success rate of 100%). Using the DIARETDB1 database, our method was able to detect the fovea center in 92.13% of all tested cases (i.e. in 82 out of 89 images). These results indicate that our approach potentially can achieve a better performance than comparable methods proposed in the literature.     

A Wavelet-Based Approach for Analyzing Industrial Stochastic Textures With Applications

Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work     

Measuring Statistical Geometric Properties of Tomographic Images of Soils

Porous media modeling is relevant in several applications, such as agricultural engineering, where soil compaction analysis requires the estimation of soil transport properties. For example, the prediction of root growing patterns and their environmental impact is usually measured by analyzing soil fluid infiltration capacity and water retention. Recently, tomographic images have been used in nondestructive tests of soil. However, using such images is challenging for two reasons: (1) Tomographic images are usually noisy, which complicates their segmentation, and (2) modeling the soil structure requires establishing adjacency relations among neighboring tomographic slices, which has a significant computational cost due to the combinatorial nature of this problem. In this paper, we propose a solution for both problems. The experimental results show that soil samples can be analyzed and classified with significant accuracy using our proposed approach.     

Adaptive image denoising using scale and space consistency

This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained. Joint use of space and scale consistency is applied for better preservation of edges. The shrinkage functions are combined to preserve edges that appear simultaneously at several resolutions, and geometric constraints are applied to preserve edges that are not isolated. The proposed technique produces a filtered version of the original image, where homogeneous regions appear separated by well-defined edges. Possible applications include image presegmentation, and image denoising.     

Feature selection for face recognition based on multi-objective evolutionary wrappers

Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.     

Stochastic texture image estimators for local spatial anisotropyand its variability

A new image analysis technique is proposed for the evaluation of local anisotropy and its variability in stochastic texture images. It utilizes the gradient function to provide information on local anisotropy, from two-dimensional (2-D) density images for foil materials like polymer sheets, nonwoven textiles, and paper. Such images can be captured by radiography or light-transmission; results are reported for a range of paper structures, and show that the proposed technique is more robust to unfavorable imaging conditions than other approaches. The method has potential for on-line application to monitoring and control of anisotropy and its variability, as well as local density itself, in continuous manufacturing processes     

Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

This correspondence describes a new superresolution approach for enhancing the resolution of mammographic images using complex wavelet frequency information. This method allows regions of interest of a mammographic image to be viewed in enhanced resolution while reducing the patient exposure to radiation. The proposed method exploits the structural characteristics of breast tissues being imaged and produces higher resolution mammographic images with sufficient visual fidelity that fine image details can be discriminated more easily. In our approach, the superresolution problem is formulated as a constrained optimization problem using a third-order Markov prior model and adapts the priors based on the phase variations of the low-resolution mammographic images. Experimental results indicate the proposed method is more effective at preserving the visual information when compared with existing resolution enhancement methods.     

Echocardiographic Image Sequence Segmentation and Analysis Using Self-Organizing Maps

This paper presents a new approach for echocardiographic image sequence segmentation and a practical application of the results. The proposed method uses the self-organizing maps to approximate the probability density function of the image patterns. The map is post-processed, by the k-means clustering algorithm, in order to detect groups of neurons whose weights are similar. Each segmented image of the sequence is generated by correlating its pixels and clusters found in the map. The image sequence segmented was used to measure fetal heart structures. To refine the measurements we used a border detection technique based on the least-means squares error. The segmentation procedure was validated successfully by physicians.     

A coarse-to-fine approach for segmenting melanocytic skin lesions in standard camera images

Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing step. A good definition of the lesion area and its border is very important for discriminating between benign and malignant cases. In this paper, we propose to segment melanocytic skin lesions using a sequence of steps. We start by pre-segmenting the skin lesion, creating a new image representation (channel) where the lesion features are more evident. This new channel is thresholded, and the lesion border pre-detection is refined using an active-contours algorithm followed by morphological operations. Our experimental results based on a publicly available dataset suggest that our method potentially can be more accurate than comparable state-of-the-art methods proposed in literature.