Mammogram Classification using Law's Texture Energy Measure and Neural Networks

Setiawan, Arden Sagiterry; Elysia,; Wesley, Julian; Purnama, Yudy

doi:10.1016/j.procs.2015.07.341

Cited by 77 publications

(29 citation statements)

References 5 publications

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“…The “texture energy” TE could be calculated by the variance statistic within macro window size of 9 × 9 in our training stage, which is defined as [36]

\begin{matrix} T17 \\ T E = \frac{\sum_{w_{x}} \sum_{w_{y}} F {(m, n)}^{2}}{P^{2} \times w_{x} \times w_{y}}, \end{matrix}

\begin{matrix} P^{2} = \underset{i, j}{false\sum} A {(m, n)}^{2} . \end{matrix}

…”

Section: The Proposed Methodsmentioning

confidence: 99%

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

Wan

Wang

et al. 2016

Computational Intelligence and Neuroscience

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Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

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“…The “texture energy” TE could be calculated by the variance statistic within macro window size of 9 × 9 in our training stage, which is defined as [36]

\begin{matrix} T17 \\ T E = \frac{\sum_{w_{x}} \sum_{w_{y}} F {(m, n)}^{2}}{P^{2} \times w_{x} \times w_{y}}, \end{matrix}

\begin{matrix} P^{2} = \underset{i, j}{false\sum} A {(m, n)}^{2} . \end{matrix}

…”

Section: The Proposed Methodsmentioning

confidence: 99%

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

Wan

Wang

et al. 2016

Computational Intelligence and Neuroscience

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“…LAWS utiliza un conjunto de máscaras de convolución de 5x5 para calcular la energía de textura, que se representa por un vector de nueve números para cada pixel analizado de la imagen. Existen 4 características principales que se pueden analizar: borde, nivel, mancha y ondulación (Setiawan, Elysia, Wesley, & Purnama, 2015).…”

Section: Métricas De Las Leyes De Energía De Textura (Laws)unclassified

“…Por ejemplo, el cálculo del vector L5 es resultado del promedio local ponderado, E5 vector indica bordes, el S5 detecta la mancha y R5 detecta el vector onda. LAWS resta el promedio local de cada pixel para producir una nueva imagen procesada, en la que la intensidad media de cada vecindario está próxima a cero (Setiawan et al, 2015).…”

Section: Métricas De Las Leyes De Energía De Textura (Laws)unclassified

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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“…A Radial Basis Function neural network for mammogram classification based on Grey level Cooccurrence matrix was developed in [54]. An alternative method of mammogram classification was designed using Law's Texture Energy measure as texture feature extraction [55].…”

Section: Classification and Detection Of Abnormalities In Mammogramentioning

confidence: 99%

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

Chopra¹,

Banga²

2017

ijarcs

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Content-based image Retrieval has been one of the lively research in the field of the computer science over the last decade or so. Many programs and tools have been used to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. But still a lot of work needs to be done in case of mammographic images. Breast cancer has been one of the causes of the increasing death of the European women.

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Mammogram Classification using Law's Texture Energy Measure and Neural Networks

Cited by 77 publications

References 5 publications

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

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

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

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