Imaging of breast implants-a pictorial review.

The number of women with breast implants is increasing. Radiologists must be familiar with the normal and abnormal findings of common implants. Implant rupture is a well-known complication after surgery and is the main cause of implant removal.

Influence of using manual or automatic breast density information in a mass detection CAD system.

Rationale And Objectives: The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be highlighted.

Materials And Methods: The image database used in this article is 92 mediolateral oblique (MLO) and 92 craniocaudal (CC) mammograms obtained by a full-field digital mammographic unit.

A review of automatic mass detection and segmentation in mammographic images.

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison.

A statistical approach for breast density segmentation.

Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types.

Eigendetection of masses considering false positive reduction and breast density information.

The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out.

[Risk factors for acute abdominal wall dehiscence after laparotomy in adults].

Introduction: Total evisceration consists of protrusion of the abdominal viscera due to dehiscence of all the planes of the abdominal wall after laparotomy. The greater the number of risk factors, the greater the probability of evisceration.

Patients And Method: We performed a retrospective study of patients with evisceration treated in the previous 9 years.

[Intestinal occlusion due to cecal volvulus].

Cecal volvulus is uncommon in the West and represents only 1% of cases of intestinal occlusion in adults. We describe three cases of intestinal occlusion due to cecal volvulus in two women aged 74 and 61 years old and a man aged 81 years old with multiple pathological antecedents. The patients had pain, abdominal distension and absent feces.