Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy

DETECÇÃO DE LESÕES EM MAMOGRAFIAS ATRAVÉS DA ASSIMETRIA DAS MAMAS E EXTRAÇÃO DE CARACTERÍSTICAS COM ÍNDICE DE GETIS-ORD

O câncer de mama é aquele que tem início nas células das mamas. A principal forma de prevençãoe diagnóstico precoce é através de exames de mamografia. Este trabalho tem como objetivo principalapresentar uma metodologia de auxílio à detecção de lesões em mamografias a partir da determinação de regiões suspeitas por nível de simetria. Técnicas de Processamento de Imagem foram usadas para preparar as mamografias e, em seguida, o nível de simetria entre a mama esquerda e a direita foi medido com coeficiente de correlação cruzada e distância euclidiana. O índice de Getis-Ord na sua forma geral foi usado para extrair características das imagens para treinar uma Máquina de Vetores de Suporte que classificouregiões das mamografias em lesão e não lesão. A metodologia, de modo geral, apresentou 80,11% de sensibilidade, 84,41% de especificidade e 84,38% de acurácia.Palavras-chave: Câncer de mama. Mamografia. Coeficiente de correlação cruzada. Distância euclidiana. Índice de Getis-Ord. Máquina de vetores de suporte. LESION DETECTION IN MAMMOGRAMS THROUGH THE ASYMMETRY OF THEBREASTS AND FEATURE EXTRACTION WITH INDEX GETIS-ORDAbstract: Breast cancer is one that starts in the cells of the breast. The main form of prevention and early diagnosis is through mammograms. This work has as main goal to present a methodology to aid in the detection of lesions on mammograms from the determination of suspicious regions by level of symmetry. Image processing techniques were used to prepare the mammograms and then the degree of symmetry between left and right breasts was measured using cross-correlation coefficient and Euclidean distance. The index Getis-Ord was used to extract features from images to train a Support Vector Machine which classified regions of mammograms in lesion and non-lesion. The methodology, in general, showed 80.11% sensitivity, 84.41% specificity and 84.38% accuracy.Keywords: Breast cancer. Mammography. Cross-correlation coefficient. Euclidean distance. Index Getis-Ord. Support vector machine. DETECCIÓN DE LESIONES EN LAS MAMOGRAFÍAS A TRAVÉS DE LA ASIMETRÍA DE LAS MAMAS Y EXTRACCIÓN DE CARACTERÍSTICAS CON EL ÍNDICE GETIS-ORDResumen: El cáncer de mama comienza en las células de los senos. La principal forma de prevención y diagnóstico precoz es a través de mamografías. Este trabajo tiene como objetivo principal presentar una metodología para ayudar en la detección de lesiones en las mamografías a partir de la determinación de las regiones sospechosas por nivel de simetría. Técnicas de procesamiento de imágenes se utilizaron para preparar las mamografías y luego el nivel de simetría entre el pecho izquierdo y derecho se midió utilizando el coeficiente de correlación cruzada y la distancia euclidiana. El índice Getis-Ord se utilizó para extraer características de las imágenes para formar una máquina de vectores de soporte que las regiones clasificadasde mamografías en lesión y no la lesión. La metodología, en general, mostró 80,11% de sensibilidad, especificidad 84,41% y 84,38% de precisión.Palabras clave: Cáncer de mama. Mamografía. Coeficiente de correlación cruzada. Distancia euclídea. Índice Getis-Ord. Máquina de vectores soporte

AUTOMATIC METHOD BASED ON PSO-OPTIMIZED VISION-TRANSFORMER FOR GAS DETECTION IN 2D SEISMIC IMAGES

One of the geophysical techniques most frequently utilized in the oil and gas (O\&G) sector for hydrocarbon prospecting is seismic reflection. The seismic reflection technique is essential for an estimate the location and volume of gas accumulations in various onshore fields. However, this technique generates a large amount of data, and its data acquisitions are noisy. Thus it takes a while to analyze and interpret seismic data. Computational techniques based on machine learning have been proposed considering Direct Hydrocarbon Indicators (DHIs) to assist geoscientists in such activities. In this paper, we describe a method to detect gas accumulations based on the Particle Swarm Optimization (PSO) algorithm and the Vision Transformer neural network (ViT). In the best scenario, the proposed method achieved a sensitivity of 88.60%, a specificity of 99.56%  and an accuracy of 99.37%. We present some tests performed on Parnaíba Basin and Netherlands F3-Block fields. Thus, it demonstrates that the proposed method is promising for assisting specialists in gas exploration tasks.DOI: 10.36558/rsc.v12i3.790

DETECÇÃO DE REGIÕES SUSPEITAS DE LESÃO NA MAMA EM IMAGENS TÉRMICAS UTILIZANDO SPATIOGRAM E REDES NEURAIS

Este trabalho propõe uma metodologia para identificar regiões suspeitas de lesão baseada nas assime-trias da mama esquerda e direita de imagens de termogramas. O estudo é pautado em imagens de pacientes do Hospital Universitário da Universidade Federal de Pernambuco (UFPE), capturadas por câmera infravermelha. Inicialmente as imagens são manualmente segmentadas. Em seguida, os seios são registrados usando a transformação B-spline. Além disso, como o corpo humano tem uma simetria radial das temperaturas, uma lesão, eventualmente, leva uma assimetria destas regiões, em seguida, o spatiogram é usado para identificar essas regiões assimétricas. Finalmente, apenas as regiões com temperaturas superiores à média são mantidas, com base no fato de que o câncer tem a temperatura mais elevada do que o restante mama. Após esse processo são extraídas características (Variação dos pixels, a média, o desvio padrão, o índice de Geary e Dimensão Fractal de Higuchi) para a classificação dessas regiões restantes em lesão ou não lesão utilizando-se uma rede neural artificial com perceptron em multicamadas. A metodologia apresentou 75% das regiões classificadas corretamente, indicando que o spatiogram e a média das temperaturas das regiões assimétricas são métodos bem promissores para identificação de regiões suspeitas de conter lesão.Palavras-chave: Termografia. Câncer. Spatiogram. Mama. Rede-neural.SUSPECT DETECTION OF REGIONS OF INJURY IN BREAST IN THERMAL IM-AGES USING SPATIOGRAM AND NEURAL NETWORKSAbstract: This paper proposes a methodology to identify suspicious regions of injury based on asymmetries of left and right breasts of thermograms images.. The study is based on images captured by infrared camera from patients at the University Hospital of the Federal University of Pernambuco. Initially the images are manually segmented. Then, the sinuses are recorded using the B-spline transformation. Furthermore, as the human body has a radial symmetry of temperatures, damage eventually leads asymmetry of these regions, then the spatiogram is used to identify those asymmetric.regions. Finally, only the regions with higher than average temperatures are maintained, based on the fact that the cancer has a higher temperature than the rest of the breast. After this process features are extracted (Variation of pixels, the mean, standard deviation, index Geary and Higuchi Fractal Dimension) for the classification of regions remaining in injury or no injury using an artificial neural network Multilayer perceptron. The methodology showed 75% of correctly classified regions, indicating that the spatiogram and the average temperatures of the asymmetric regions are well promises methods to identify regions suspected of containing lesion.Keywords: Thermography. Cancer. Spatiogram. Breast. Neural-network.DETECCIÓN DE ZONAS SOSPECHOSAS DE LESIÓN EN LA MAMA EN IMÁGENES TÉRMICAS UTILIZANDO SPATIOGRAM Y REDES NEURALESResumen: En este trabajo se propone una metodología para identificar las regiones sospechosas de lesión basado en las asimetrías de la mama izquierda y derecha de las imágenes termogramas. El estudio se basa en las imágenes capturadas por la cámara infrarroja de los pacientes en el Hospital Universitario de la Universidade Federal de Pernambuco. Inicialmente, las imágenes son segmentadas manualmente. Luego, los senos se registran utilizando la transformación B-spline. Además, como el cuerpo humano tiene  una simetría radial de temperaturas, daños eventualmente conducen a una asimetría de estas regiones, entonces el spatiogram se utiliza para identificar las regiones asimétricas. Finalmente, basado en el hecho de  que el cáncer tiene una temperatura más alta que el resto de la mama, sólo las regiones con temperaturas más alta que la temperatura media son mantenidas. Después de este proceso se extraen características (Variación de píxeles, la media, desviación estándar, Dimensión índice y Higuchi Geary fractal) para la clasificación de las regiones restantes en lesiones o ninguna lesión utilizando un perceptrón multicapa red neural artificial. La metodología mostró 75% regiones clasificados correctamente, lo que indica que las temperaturas spatiogram y media de las regiones son métodos asimétricos bien promete para identificar regiones sospechosas de contener lesión.Palabras clave: Termografía. Cáncer. Spatiogram. Mama. Redes neuronales

An Approach for Construction of Augmented Reality Systems using Natural Markers and Mobile Sensors in Industrial Fields

This paper presents a methodology for the development of augmented reality (AR) visualization applications in industrial scenarios. The proposal presents the use of georreferenced natural markers detected in real time, which enables the construction of AR systems. This use of augmented visualization allows the creation of tools that can aid on-site maintenance activities for operators. AR use makes possible including information about the equipment during a specific procedure. In this work, the detection of natural markers in the scene are based on Haar-like features associated with equipment geolocalization. This approach enable the detection of equipment in multiple user’s viewpoints in the industrial scenario and makes it possible the inclusion of real information about those equipment in real time as AR annotations. In this way, beyond a methodology approach, this paper presents a new way for Power System information visualization in the field that can be used in both for training and for control operations

Taxonomic indexes for differentiating malignancy of lung nodules on CT images

Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published