Optimized Gabor features for mass classification in mammography

Khan, Salabat; Hussain, Muhammad; Aboalsamh, Hatim; Mathkour, Hassan; Bebis, George; Zakariah, Mohammed

doi:10.1016/j.asoc.2016.04.012

Cited by 52 publications

(25 citation statements)

References 35 publications

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“…Este filtro es el más utilizado en el ámbito de preprocesamiento de la imagen debido a la eficiencia de la descripción de textura de las imágenes de mamografías según (Barrena & Valls, 2014). El propósito general del filtro es la descomposición de una imagen en múltiples escalas y orientaciones para lograr el análisis de los patrones de texturas (Khan et al 2016;Tusa et al 2014). Según (Khan et al, 2016), los filtros Gabor representan diferentes escalas y orientaciones en micropatrones de ROIs.…”

Section: Filtros Gaborunclassified

“…El propósito general del filtro es la descomposición de una imagen en múltiples escalas y orientaciones para lograr el análisis de los patrones de texturas (Khan et al 2016;Tusa et al 2014). Según (Khan et al, 2016), los filtros Gabor representan diferentes escalas y orientaciones en micropatrones de ROIs. La fórmula general de los filtros de Gabor 2D está compuesta por un centro gaussiano modulado por una exponencial compleja (Barrena & Valls, 2014;Torrents-Barrena, Puig, Melendez, & Valls, 2016).…”

Section: Filtros Gaborunclassified

“…Las SVM son muy sensibles al ruido presente en los datos y son propensos a la generalización excesiva. Además, se puede degradar el rendimiento de la clasificación con el fin de atender este déficit (Khan et al, 2016). El clasificador toma la entrada del vector que es mapeado en un espacio de características de forma lineal o no lineal (Barrena, 2014), que está relacionado con la función del núcleo.…”

Section: Máquinas De Vectores De Soporte (Svm)unclassified

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Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

et al. 2018

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RESUMENLa presente revisión de esquemas de clasificación analiza los algoritmos y métodos más utilizados dentro de la detección automática y clasificación supervisada de anomalías en imágenes de mamografías. Para ello, se propone el análisis de 36 artículos científicos en base a su metodología de clasificación, considerando además que estas publicaciones se encuentran indexadas en revistas reconocidas en los últimos cinco años. El estudio se ha dividido en cuatro etapas. La primera etapa es el preprocesamiento, que incluye las bases de datos utilizadas para entrenamiento y pruebas. Además, describimos los métodos de filtrados como la ecualización de histogramas (escala de grises) y el filtro Gabor, entre otros métodos que han brindado resultados eficientes. La etapa de los algoritmos de extracción de características presenta coincidencias en la utilización de la transformada discreta de coseno y la transformada discreta de ondícula entre otros algoritmos que se han utilizado para esta tarea. Para los algoritmos de clasificación, se resalta la utilización de las máquinas de vectores de soporte. En adición a la revisión de expresiones matemáticas desarrolladas en esta investigación, una de las contribuciones de este trabajo reside en proporcionar una lista de clasificadores híbridos, que resultan de la combinación de los clasificadores convencionales. Finalmente, se describen las métricas de evaluación como la curva receptora de características operativas y la precisión, que son indicadores cuantitativos del desempeño de los algoritmos revisados en este trabajo investigativo.PALABRAS CLAVE: Algoritmo de clasificación, cáncer de mama, extracción de características, mamografía, reconocimiento de patrones. ABSTRACTThe present revision of classification schemes analyzes the algorithms and methods most used within the automatic detection and supervised classification of abnormalities in mammography images. To do this, we propose the analysis of 36 scientific papers based on their classification methodology, considering also that these publications are indexed in recognized journals in the last five years. The study has been divided into four stages. The first stage is preprocessing, which includes databases used for training and testing. In addition, we describe the filtering methods such as histogram equalization (grayscale) and the Gabor filter, among other methods that have yielded efficient results. The stage of feature extraction algorithms presents coincidences in the use of the discrete cosine transform and the discrete wavelet transform, among other algorithms that have been used for this task. For the classification algorithms, we highlight the use of the support vector machine. In addition to the review of mathematical expressions developed in this research, one of the contributions of this paper is to provide a list of hybrid classifiers, which result from the combination of conventional classifiers. Finally, we describe the evaluation metrics such as the receiver operating characteristic curve and the precis...

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Section: Filtros Gaborunclassified

Section: Máquinas De Vectores De Soporte (Svm)unclassified

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Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

et al. 2018

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“…where g k ðhÞ is the variogram amount with different ranges of variogram h, DN is the values of the grey level pixels of x i and x i C h and nðhÞ is the number of pairs of points at a distance h in an area inside the image. Gabor features: each Gabor filter is a linear filter output which is defined by multiplying a harmonic function in a Gaussian function (Khan et al 2016…”

Section: Texture Analysismentioning

confidence: 99%

A GIS-based approach for earthquake loss estimation based on the immediate extraction of damaged buildings

Ranjbar

Dehghani

Ardalan

et al. 2016

Geomatics, Natural Hazards and Risk

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2017) A GIS-based approach for earthquake loss estimation based on the immediate extraction of damaged buildings, Geomatics, Natural Hazards and Risk, 8:2, 772-791, ABSTRACTWith the remote sensing technology is possible to predict estimations of the expected number of killed or injured people. In this study, a loss estimation method is proposed by combining data from remote sensing technology and geographic information system (GIS). For the image processing, we assume the damage area with a rougher texture when compared with undamaged areas. Extracted textures were later considered as the decision-making basis for the building damage level determination. We prepared the GIS database with information on the land use, structural material and building occupancy. Considering the influence of time in the building occupancy, two periods were considered (activity and inactivity) for each land use. The model estimates casualties based on the population residing in each building and according to the time of the earthquake, structure material of each building and destruction percentage. The approach was tested on Ahar and Varzaghan cities of Iran using 0.5 m resolution pan-sharpened Geo-Eye 1 imagery. The root-mean-square error (RMSE) for the number of injured and killed people was determined, considering field teams' data and predicted data. RMSE for injured population was estimated in 0.040 and RMSE for fatal casualties was estimated in 0.025. KEYWORDSHigh spatial resolution satellite image; texture; fuzzy inference system; GENIE algorithm; casualty estimation model CONTACT Hamid Reza Ranjbar

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“…On the other hand, the process of obtaining the optimal parameters of Gabor filter can be seen as a combinatorial optimization problem that can be tackled by swarm intelligence algorithms. For example, Khan [19,20] presented a technique for the optimization of each filter individually via particle swarm optimization (PSO) and cuckoo search (CS) algorithm, and the approach successfully represents local texture changes at multiple scales and orientations from mammograms with an effective improvement in classification accuracy. Tong [21] proposed a defect detection technique via Gabor filter to inspect flaws in woven fabrics in the fashion industry, and the differential evolution (DE) algorithm was utilized to obtain the optimal parameters of the filter bank.…”

Section: Introductionmentioning

confidence: 99%

A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

et al. 2019

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Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

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Optimized Gabor features for mass classification in mammography

Cited by 52 publications

References 35 publications

Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

Revisión de esquemas de clasificación de tejidos cancerígenos en imágenes de mamografía

A GIS-based approach for earthquake loss estimation based on the immediate extraction of damaged buildings

A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

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