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This work presents the usefulness of texture features in the classification of breast lesions in 5,518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.

OBJETIVO: Caracterizar lesões intersticiais em radiografias frontais de tórax, com base na análise de atributos estatísticos de textura, os quais permitem detectar sinais de anormalidades com natureza difusa. MATERIAIS E MÉTODOS: O esquema começa com a segmentação semi-automática dos campos pulmonares, sendo o contorno externo marcado manualmente, com posterior divisão automática de cada pulmão em seis regiões. O banco de imagens utilizado neste trabalho é composto por 482 regiões obtidas de exames contendo lesões e 324 regiões obtidas de exames normais. Os atributos de textura são extraídos automaticamente de cada uma dessas regiões e uma seleção das melhores combinações de atributos é feita através da distância Jeffries-Matusita. A classificação das regiões em normal ou suspeita é feita pela comparação com os k vizinhos mais próximos e o treinamento do classificador é baseado na técnica de treino e teste "half-half" e correlação cruzada. RESULTADOS: Os resultados obtidos foram analisados através do valor da área sob a curva ROC ("receiver operating characteristic"), a qual indica um sistema perfeito para uma área igual a 1. Os resultados forneceram uma área sob a curva ROC (A Z) igual a 0,887, com valores de sensibilidade igual a 0,804 e especificidade igual a 0,793. CONCLUSÃO: Os resultados indicam que o sistema de caracterização baseado em atributos de textura possui bom potencial para o auxílio ao diagnóstico de lesões intersticiais de pulmão.OBJECTIVE: To characterize interstitial lesions in anterior-posterior chest X-rays based on the analysis of textural statistical features that allow the detection of abnormalities with diffuse pattern. MATERIALS AND METHODS: Image analysis begins with the semiautomatic segmentation of the lungs, marking the external contour of the lung manually followed by an automatic division of each lung in six regions. The data base of images used in this study consisted of 482 regions obtained from examinations in which lesions were detected and 324 regions from normal examinations. Textural features were automatically extracted from each area and the selection of the best set of features was made based on the Jeffries-Matusita distance. The regions were classified as normal or suspected using the k nearest-neighbor method and half-half, and cross-correlation methodologies were used for training the classifier. RESULTS: Results were assessed based on the value of the area under the ROC (receiver operating characteristic) curve that indicates an ideal response for an area equal to 1. The results showed an area under the ROC curve (A Z) of 0.887, sensitivity of 0.804, and specificity of 0.793. CONCLUSION: These results indicate that the implemented system has a good potential for computer-aided diagnosis of interstitial lung lesions.

We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate, and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular, nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest images has the potential to assist radiologists in their interpretation of diffuse lung diseases.

This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick’s texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.

OBJETIVO: Caracterizar lesões intersticiais em radiografias frontais de tórax, com base na análise de atributos estatísticos de textura, os quais permitem detectar sinais de anormalidades com natureza difusa. MATERIAIS E MÉTODOS: O esquema começa com a segmentação semi-automática dos campos pulmonares, sendo o contorno externo marcado manualmente, com posterior divisão automática de cada pulmão em seis regiões. O banco de imagens utilizado neste trabalho é composto por 482 regiões obtidas de exames contendo lesões e 324 regiões obtidas de exames normais. Os atributos de textura são extraídos automaticamente de cada uma dessas regiões e uma seleção das melhores combinações de atributos é feita através da distância Jeffries-Matusita. A classificação das regiões em normal ou suspeita é feita pela comparação com os k vizinhos mais próximos e o treinamento do classificador é baseado na técnica de treino e teste "half-half" e correlação cruzada. RESULTADOS: Os resultados obtidos foram analisados através do valor da área sob a curva ROC ("receiver operating characteristic"), a qual indica um sistema perfeito para uma área igual a 1. Os resultados forneceram uma área sob a curva ROC (A Z) igual a 0,887, com valores de sensibilidade igual a 0,804 e especificidade igual a 0,793. CONCLUSÃO: Os resultados indicam que o sistema de caracterização baseado em atributos de textura possui bom potencial para o auxílio ao diagnóstico de lesões intersticiais de pulmão. OBJECTIVE: To characterize interstitial lesions in anterior-posterior chest X-rays based on the analysis of textural statistical features that allow the detection of abnormalities with diffuse pattern. MATERIALS AND METHODS: Image analysis begins with the semiautomatic segmentation of the lungs, marking the external contour of the lung manually followed by an automatic division of each lung in six regions. The data base of images used in this study consisted of 482 regions obtained from examinations in which lesions were detected and 324 regions from normal examinations. Textural features were automatically extracted from each area and the selection of the best set of features was made based on the Jeffries-Matusita distance. The regions were classified as normal or suspected using the k nearest-neighbor method and half-half, and cross-correlation methodologies were used for training the classifier. RESULTS: Results were assessed based on the value of the area under the ROC (receiver operating characteristic) curve that indicates an ideal response for an area equal to 1. The results showed an area under the ROC curve (A Z) of 0.887, sensitivity of 0.804, and specificity of 0.793. CONCLUSION: These results indicate that the implemented system has a good potential for computer-aided diagnosis of interstitial lung lesions.

The aim of this work was to determine a methodology to selection of the best features subset and artificial neural network (ANN) topology to classify masses lesions. The backpropagation training algorithm was used to adjust the weights of ANN. A total of 118 regions of interest images were chosen (68 benign and 50 malignant lesions). In a first step, images were submitted to a combined process of thresholding, mathematical morphology, and region growing techniques. After, fourteen texture features (Haralick descriptors) and fourteen shape features (circularity, compactness, Gupta descriptors, Shen descriptors, Hu descriptors, Fourier descriptor and Wee descriptors) were extracted. The Jeffries-Matusita method was used to select the best features. Three shape features sets and three texture features sets were selected. The Receiver Operating Characteristic (ROC) analyses were conducted to evaluated the classifier performance. The best result for shape feature set was accurate classification rate of 98.21%, specificity of 98.37%, sensitivity of 98.00% and the area under ROC curve of 0.99, for a ANN with 5 hidden units. The best result for texture feature set was accurate classification rate of 97.08%, specificity of 98.53%, sensitivity of 95.11% and the area under ROC curve of 0.98, for an ANN with 4 hidden units.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.