Mammogram image visual enhancement, mass segmentation and classification

Al-Najdawi, Nijad; Biltawi, Mariam; Tedmori, Sara

doi:10.1016/j.asoc.2015.06.029

Cited by 79 publications

(37 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the low-contrast problem, the digitization noise in mammograms also affects the success of ROI detection negatively; and noise reduction is required to improve the image quality [7, 8]. Hence, preprocessing is necessary and should be the first of the four stages in a CAD system.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Esener

Ergin

Yüksel

2017

Journal of Healthcare Engineering

View full text Add to dashboard Cite

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Esener

Ergin

Yüksel

2017

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…An image selection process should be incorporated in to better target problematic images [12]. In some circumstances, various enhancement algorithms can be combined together to achieve better results for the segmentation of the breast region and visual interpretation, analysis, and classification of mammogram masses to assist the radiologist in making more accurate decisions [13].…”

Section: Preprocessing Of Mammographic Imagesmentioning

confidence: 99%

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

Chopra¹,

Banga²

2017

ijarcs

View full text Add to dashboard Cite

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.

show abstract

“…In 2015, the scholars published a paper about mammogram image visual enhancement, mass segmentation and classification [4]. First, the noise was removed and the breast region was segmented using contour segmentation.…”

Section: Related Workmentioning

confidence: 99%

The Identification of EGFR Mutation Based on the CT Image of Lung Adenocarcinoma

Tsan¹,

Tsai²,

Chin³

2016

IJIEE

View full text Add to dashboard Cite

Abstract-Lung adenocarcinoma is the most common cause of lung cancer in Taiwan. However, it is difficult to detect lung adenocarcinoma at early stage. Most radiologists usually decide the treatment strategies by screening whether the patients have the epidermal growth factor receptor (EGFR) gene mutations or not. However, the screen is expensive and time-consuming. Therefore, the aim of the present paper is to identify whether there are EGFR mutations in tumors with CT image. We segment the whole region of lung adenocarcinoma and extract the texture features in spatial domain and frequency domain using GLCM and Gabor Wavelet. Finally we use Neuro-fuzzy based AdaBoost to classify and identify the relationship between the texture features of lung adenocarcinoma CT images and EGFR mutations. The experimental result in this paper shows that our success rate was 92%, and we will improve our methods to increase the recognition rate in the future.

show abstract

Mammogram image visual enhancement, mass segmentation and classification

Cited by 79 publications

References 54 publications

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

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

The Identification of EGFR Mutation Based on the CT Image of Lung Adenocarcinoma

Contact Info

Product

Resources

About