Michele F. Angelo

Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes

Breast cancer is one of the most important cause to mortality rate among women. Computer-Aided Detection (CAD) schemes have been developed as a tool in detecting early breast cancer. This can be an important tool in mammography since previous studies have been indicated that the detection of breast cancer can be increased up to 20% when assisted by a CAD scheme. One of the main stages of such process is thus the segmentation of structures of interest, as the suspect masses. However, when evaluating mammograms obtained from dense breasts, a CAD scheme efficacy can be very reduced due to the poor contrast of such type of image. This work attempts hence to this challenge, by describing a methodology for segmenting suspect masses in dense breast images as a part of a CAD scheme under development. This methodology is based on the Watershed transformation, which is combined with two other procedures -- a histogram equalization, working as pre-processing for enhance images contrast, and a labeling procedure intended to reduce noise. Tests with a set of 252 regions of interest extracted from 130 digitized mammograms have registered a scheme sensibility of 92% with about 90% of specificity. These results are promising when applied to dense breast images, which can improve significantly the performance of a processing scheme for such type of cases in mammography.

Recognition of Static Gestures Applied to Brazilian Sign Language (Libras)

This paper aims at describing an approach developed for the recognition of gestures on digital images. In this way, two shape descriptors were used: the histogram of oriented gradients (HOG) and Zernike invariant moments (ZIM). A feature vector composed by the information acquired with both descriptors was used to train and test a two stage Neural Network, which is responsible for performing the recognition. In order to evaluate the approach in a practical context, a dataset containing 9600 images representing 40 different gestures (signs) from Brazilian Sign Language (Libras) was composed. This approach showed high recognition rates (hit rates), reaching a final average of 96.77%.

Comparing Mammographic Images

The main motivation in using intensity attributes to analyze and compare the technologies of mammography images acquisition is that such images are intrinsically of low contrast, which hinders the lesions detection and interpretation. Considering the viewing conditions, the study is under development to validate the influence of the contrast variation of many types of equipment on the radiologists diagnosis. However, for the image processing, the contrast variation can be considered a problem affecting the detection of important structures. A large-intensity variation in a digitized image can be manipulated by image-processing techniques and aid in lesion detection. However, low-intensity variation, as observed in the images of full-field digital mammography systems, can impede the detection of some lesions types, as masses are structures of low contrast. Therefore, the new technologies should be tested and validated for an adequate calibration according to the procedures adjacent to the image acquisition itself.

A prototype of mammography CADx scheme integrated to imaging quality evaluation techniques

As all women over the age of 40 are recommended to perform mammographic exams every two years, the demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical practice testing, with results comparable to others reported in literature.

Avaliação de desempenhos de esquema de diagnóstico auxiliado por computador (CAD) para diferentes grupos de imagens mamográficas

Este trabalho descreveu os testes de um esquema de diagnostico auxiliado por computador (CAD) com dois diferentes grupos de imagens mamograficas e comparou com o desempenho das respostas dos especialistas. Foram utilizadas imagens com comprovacoes patologicas com regioes de interesse (RIs) com nodulos benignos e malignos. O grupo 1 de imagens foi composto por 102 RIs apenas com nodulos malignos, e o grupo 2 por 50 RIs, contendo nodulos benignos e malignos. As imagens do grupo 1 passaram por dupla leitura de especialistas e suas respostas foram comparadas com as do CAD. O CAD apresentou area sob a curva ROC (AZ ) de 0,94 e 0,84 para os grupos 1 e 2 respectivamente. Enquanto os especialistas apresentaram AZ de 0,85 para o grupo1.

Tumoral mass classification by specialists and the CAD scheme

This work consists of the validation of a Computer-Aided Diagnostic (CAD) scheme for mammographic images that to be adopted in the digital mammographic image interpretation training system for radiology residents at UNIFESP. It is a refinement phase of a CAD, developed by the LAPIMO (Laboratory of Medical and Odontology Images Processing and Analysis) group from EESC / USP, Sao Carlos. As a part of the CAD validation, digital mammograms were used, with double reading by specialists, all with pathological confirmation of lesions. 102 malignant lesions were used and the specialists responses were compared to those given by the CAD, both of them according to BI-RADS standards. To evaluate the CAD performance, the BIRADS categories 0, 4 and 5 were considered as positive lesions and the categories 1, 2 and 3 as negative. Comparing the CAD readings to those of the specialists, we obtained the following results: true-positive (TP) - 0.84 and 0.74, respectively; false-negative (FN) - 0.16 and 0.26, respectively. The agreement between the CAD readings and those of the specialists was of approximately 61%. The CAD and specialists FN readings disagreed in approximately 90% of the cases. At this validation phase, the CAD’s contribution to the FN reduction as a second opinion in the diagnosis became clear.