Computerized detection of malignant tumors on digital mammograms

Kobatake, Hidefumi; Murakami, Masayuki; Takeo, Hideya; Nakamura, Shigeru

doi:10.1109/42.774164

Cited by 115 publications

(40 citation statements)

References 15 publications

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“…Finally, the tumor's contour is estimated by using the gradient vector flow snake. Kobatake et al [6] proposed the iris filter to detect lesions as suspicious regions with a low contrast compared to their background. The proposed filter has the features' extraction ability of malignant tissues.…”

Section: Introductionmentioning

confidence: 99%

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Abdel‐Qader

Abu-Amara

2008

Modelling and Simulation in Engineering

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Recommended by Ewa PietkaScreening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3-4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Abdel‐Qader

Abu-Amara

2008

Modelling and Simulation in Engineering

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“…Difference of Gaussians and derivative based feature saliency is employed in [13] and relative image intensity was used in [15] for the detection of masses. An iris filter is used to detect tumors in [8]. Some studies have justified the use of model based image processing techniques such as Markov random field and statistical model is used in [9] for abnormal area detection.…”

Section: Previous Workmentioning

confidence: 99%

Pectoral Muscle removal and Detection of masses in Digital Mammogram using CCL

Subashini¹,

Ramalingam²,

Palanivel³

2010

IJCA

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A mammogram is a radiograph of the breast tissue. It is an effective non-invasive means of examining the breast, commonly searching for breast cancer. Cancer is not preventable, but early detection leads to a much higher chance of recovery and lowers the mortality rate. Due to the high volume of images to be analyzed by radiologists, and since senior radiologists are rare, the accuracy rate tends to decrease. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or poor diagnosis. This paper proposes a computer aided diagnosis system for detecting masses in mammograms using connected component labeling(CCL). This paper also addresses the problem of eliminating and pectoral muscles from the mammogram before the detection process so that further processing is confined to the breast region alone.

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“…Currently, GLCM has been applied to characterize most malignant tumors, script identi¯cation, facial expression recognition, etc. 10,[20][21][22] There are several important parameters to consider when designing a GLCM, which are as follows: (1) the region size, (2) the quantization levels, N g , (2) the displacement value d, and (3) the orientation value . The region size gives the dimensions of the region of which GLCM is computed.…”

Section: Glcmmentioning

confidence: 99%

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

Jusman

Hasikin‬

et al. 2017

J. Innov. Opt. Health Sci.

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This study develops a novel cervical precancerous detection system by using texture analysis of¯eld emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The¯rst step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the¯rst step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To di®eren-tiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To con¯rm the system's performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and speci¯city of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

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Computerized detection of malignant tumors on digital mammograms

Cited by 115 publications

References 15 publications

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Pectoral Muscle removal and Detection of masses in Digital Mammogram using CCL

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

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