Orazio Gambino

Automatic Volumetric Liver Segmentation Using Texture Based Region Growing

In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been performed on both basal phase and arterial phase images. Segmentation result shows the effectiveness of the proposed method: liver organ is correctly recognized and segmented, leaving out liver vessels form the segmented area and overcoming the “organ-splitting” problem. The goodness of the proposed method has been confirmed by manual liver segmentation results, having analogous and super-imposable behavior.

Blood vessels and feature points detection on retinal images

In this paper we present a method for the automatic extraction of blood vessels from retinal images, while capturing points of intersection/overlap and endpoints of the vascular tree. The algorithm performance is evaluated through a comparison with handmade segmented images available on the STARE project database (STructured Analysis of the REtina). The algorithm is performed on the green channel of the RGB triad. The green channel can be used to represent the illumination component. The matched filter is used to enhance vessels w.r.t. the background. The separation between vessels and background is accomplished by a threshold operator based on gaussian probability density function. The length filtering removes pixels and isolated segments from the resulting image. Finally endpoints, intersections and overlapping vessels are extracted.

Exponential Entropy Driven HUM on Knee MR Images

A very important artifact corrupting magnetic resonance images is the RF inhomogeneity. This kind of artifact generates variations of illumination which trouble both direct examination by the doctor and segmentation algorithms. Even if homomorphic filtering approaches have been presented in literature, none of them has developed a measure to determine the cut-off frequency. In this work we present a measure based on information theory with a large experimental setup aimed to demonstrate the validity of our approach

Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity

Brain MR Images corrupted by RF-Inhomogeneity exhibit brightness variations in such a way that a standard Fuzzy C-Means (fcm) segmentation algorithm fails. As a consequence, modified versions of the algorithm can be found in literature, which take into account the artifact. In this work we show that the application of a suitable pre-processing algorithm, already presented by the authors, followed by a standard fcmsegmentation achieves good results also. The experimental results ones are compared with those obtained using SPM5, which can be considered the state of the art algorithm oriented to brain segmentation and bias removal.

Multi-modal non-rigid registration of medical images based on mutual information maximization

In this paper, a new multi-modal non-rigid registration technique for medical images is presented. Firstly, the registration problem is outlined and some of the most common approaches reported, then, the proposed algorithm is presented. The proposed technique is based on mutual information maximization and computes a deformation field through a suitable globally smoothed affine piecewise transformation. The algorithm has been conceived with particular attention to computational load and accuracy of results. Experimental results involving intra-patient, inter-patients and atlas images on brain CT and MR (T1, T2 and PD modalities) are reported.

Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation

In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.

Noise Filtering Using Edge-Driven Adaptive Anisotropic Diffusion

This paper presents a method aimed to noise removal in MRI (magnetic resonance imaging). We propose an improvement of Perona and Maliks anisotropic diffusion filter. In our schema, the diffusion equation of the filter has been modified to take into account the edges direction. This allows the filter to blur uniform areas, while it better preserves the edges. Both quantitative and qualitative evaluation is presented and the results are compared with other methods.

Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data

Abstract In this work a general framework for non-rigid 3D medical image registration is presented. It relies on two pattern recognition techniques: kernel regression and fuzzy c-means clustering. The paper provides theoretic explanation, details the framework, and illustrates its application to implement three registration algorithms for CT/MR volumes as well as single 2D slices. The first two algorithms are landmark-based approaches, while the third one is an area-based technique. The last approach is based on iterative hierarchical volume subdivision, and maximization of mutual information. Moreover, a high performance Nvidia CUDA based implementation of the algorithm is presented. The framework and its applications were evaluated with a number of tests, which show that the proposed approaches achieve valuable results when compared with state-of-the-art techniques. Additional assessment was taken by expert radiologists, providing performance feedback from the final user perspective.

An enhanced random walk algorithm for delineation of head and neck cancers in PET studies.

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.

Accessibility of the Italian institutional web pages: a survey on the compliance of the Italian public administration web pages to the Stanca Act and its 22 technical requirements for web accessibility

Accessibility of the Italian public administration web pages is ruled by the Stanca Act and in particular the Decree of the Minister issued on July 8, 2005. In this paper, an objective test is performed on the official web pages of the Italian province and region chief towns to check their compliance to the 22 technical requirements defined by the Stanca Act. A sample of 976 web pages belonging to the websites of the Italian chief towns have been downloaded in the period October–December 2012. Such a data collection has been submitted to Achecker, the worldwide recognized syntax and accessibility validation service. Several accessibility and syntax errors have been found following the automatic analysis. Such errors have been classified, a statistic has been produced, and some graphs are included to offer an immediate view of the error distribution. Moreover, the most frequent errors are pointed out and explained in detail. Although the Stanca Act has been promulgated some years ago, and contains precise indications about updating a web page to be compliant to the 22 technical requirements, all the analyzed websites are not fully compliant to the law. Updating web pages to be compliant to the Stanca Act is a slow process and some grave errors are still present, both in terms of syntax and accessibility.