Fátima L. S. Nunes

Building a Open Source Framework for Virtual Medical Training

This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.

Contrast enhancement in dense breast images to aid clustered microcalcifications detection.

This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

Using concepts of content‐based image retrieval to implement graphical testing oracles

Automation of testing is an essential requirement to render it viable for software development. Although there are several testing techniques and criteria in many different domains, developing methods to test programs with complex outputs remains an unsolved challenge. This setting includes programs with graphical output, which produce images or interface windows. One possible approach towards automating the testing activity is the use of automatic oracles in which a reference image, taken as correct, can be used to establish a correctness measure in the tested program execution. A method that uses concepts of content‐based image retrieval to facilitate oracle automation in the domain of programs with graphics output is presented. Two case studies, one using a computer‐aided diagnostic system and one using a Web application, are presented, including some reflections and discussions that demonstrate the feasibility of the proposed approach. Copyright

Contrast enhancement in dense breast images using the modulation transfer function

This work proposes a method aimed at enhancing the contrast in dense breast images in mammography. It includes a new preprocessing technique, which uses information on the modulation transfer function (MTF) of the mammographic system in the whole radiation field. The method is applied to improve the efficiency of a computer-aided diagnosis (CAD) scheme. Seventy-five regions of interest (ROIs) from dense mammograms were acquired in two pieces of equipment (a CGR Senographe 500t and a Philips Mammodiagnost) and were digitized in a Lumiscan 50 laser scanner. A computational procedure determines the effective focal spot size in each region of interest from the measured focal spot in the center for a given mammographic equipment. Using computational simulation the MTF is then calculated for each field region. A procedure that enlarges the high-frequency portion of this function is applied and a convolution between the resulting new function and the original image is performed. Both original and enhanced images were submitted to a processing procedure for detecting clustered microcalcifications in order to compare the performance for dense breast images. ROIs were divided into four groups, two for each piece of equipment-one with clustered microcalcifications and another without microcalcifications. Our results show that in about 10% of the enhanced images more signals were detected when compared to the results for the original dense breast images. This is important because the usual processing techniques used in CAD schemes present poor results when applied to dense breast images. Since the MTF method is a well-recognized tool in the evaluation of radiographic systems, this new technique could be used to associate quality assurance procedures with the processing schemes employed in CAD for mammography.

Virtual reality framework for medical training: implementation of a deformation class using Java

Framework consists of softwares construction technique which facilitates the reuse of analysis, design, code and tests. This article presents a proposal for objects-oriented Frameworks construction for Virtual Reality (VR) applications on medical training, propitiating the most efficient and faster development of applications in this area. The initial approach is the applications breasts aspiration examination, where is necessary an object to represent the human organs and another one to simulate the necessary equipment to collect material. Functionalities like a collisions detection with accuracy, stereoscopy and deformation, were initially foreseen and already implemented for the framework construction. As medical trainings tools, its importance is more emphasized, since it has reactions to the users action. The deformation technique in three-dimensional objects was implemented in API Java 3D and its development based on Mass Springs method, aiming at to supply a class to be used in medical trainings tools.

Investigation of clustered microcalcification features for an automated classifier as part of a mammography CAD scheme

Classification of breast microcalcifications and clusters depends characteristics selected to be the input for an automated classifier. Artificial neural networks have been used to aid in classification of structures on mammograms images. However, to achieve the classification, some attributes have to be adequately extracted from the images in the database used for tests. As a part of a CAD scheme in development, our intention is to establish a ANN-based classifier, intended to distribute detected clustered microcalcifications in one of 5 classes, according to BI-RADS classification (normal, benign, probably benign, suspicious and probably malignant). This work reports a part of this procedure, by extracting and selecting most of significant characteristics regarding digitized mammography images containing clustered microcalcifications. Two distinct classes-probably benign and suspicious-are considered in order to compare the selected characteristics incidence distribution. Distance between both classes could be estimated by using Gaussian curves. Images used for the tests were from a database composed by mammograms digitized with 600 dpi of spatial resolution in a andbit grayscale. The regions of interest were selected based on physicians reports on the existence of a cluster. This study has shown that characteristics just as irregularity, number of microcalcifications in a cluster, and cluster area are already enough to separate the processed images in two very distinct classes-suspicious and probably benign, although other features could be necessary for a more detailed classification.

Performance of a processing scheme for clustered microcalcifications detection with different images database

Although many researchers have reported high efficacy rates of some computer-aided diagnosis (CAD) schemes, it is known that their performance depends strongly on the database used for the tests. This is an important task to the comparison of different schemes performance, since they usually are developed and tested with a particular images set. Previously, we have reported the development of an image processing scheme designed to detect clustered microcalcifications as a part of a CAD scheme. Therefore, in this work we are reporting the performance results of such a processing procedure after tests with three different image databases: two corresponding to mammograms obtained from Hospital das Clinicas de Ribeirao Preto, Brazil, but from different units and digitizied in different scanners; and a third corresponding to mammographic images obtained directly in digital form by Internet from the National Expert and Training Centre for Breast Cancer Screening at the University of Nijmegen, the Netherlands. As expected, the scheme efficacy in detecting clusters has changed according to the images set tested: from 95% of efficacy (true positive plus true negative results) for the best situation to 88% for the worst one. In addition, we discuss briefly some factors relative to the images sets which can change a CAD scheme results.

Evaluation of VR medical training applications under the focus of professionals of the health area

This paper presents part of the implementation of a Virtual Reality (VR) framework, involving the building of an interaction module with support to conventional and non-conventional devices, and the evaluation of a system prototype, considering computational and human aspects. The proposal of the evaluation is to improve the framework, and consequently, the applications generated by it, following ideas and opinions of healths professionals (doctors and students), who are the target public of this project.

A method to contrast enhancement of digital dense breast images aimed to detect clustered microcalcifications

Computer-aided diagnosis (CAD) schemes have been developed in many research centers to help the early detection of breast cancer. However, dense breast images are a challenge to CAD schemes due to the low contrast between structures of interest (such as microcalcifications-small size structures-which usually are associated to several breast tumors) and the background. This work describes a method to eliminate the background of a digitized mammogram image as well as two specific techniques to enhance the contrast in dense breast digital images as part of a CAD scheme under development in our group. The results indicate that these techniques can improve the performance of the scheme, and, thus, it can help in the early detection of breast cancer.

Investigations on the effect of different characteristics of images sets on the performance of a processing scheme for microcalcifications detection in digital mammograms.

The performance of a computer-aided dianosis (CAD) scheme is closely dependent on the database used for its development and tests. The scheme sensitivity can be reduced by 15% to 25%, with only 20% of changes in the database cases. Previously, we have developed a processing scheme in order to detect clustered microcalcifications in digital mammograms, and we have tested such a procedure with two different databases. Further evaluations in developing a CAD scheme for mammography have indicated the need for more extensive investigation on the effects resulting from different characteristics of the images bank used for tests. Therefore, this work reports some results regarding such an investigation, with a further discussion over characteristics that can affect the performance of a CAD scheme.