ROI segmentation using Local Binary Image

Sharma, Shubhi; Khanna, Pritee

doi:10.1109/iccsce.2013.6719947

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Otsu multilevel thresholding is a global thresholding method proposed by Nobuyuki Otsu [29]. In this method, automatic thresholding is performed by using zero and first-order cumulative moments of the gray-level histogram [44]. Let [ ] be the gray levels of an input image, be the pixel number of the 'th gray value and assume that a threshold value, , is selected to separate the image into two regions as and .…”

Section: ) Otsu Multilevel Thresholdingmentioning

confidence: 99%

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Toz

Erdoğmuş

2021

IEEE Access

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Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.

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Section: ) Otsu Multilevel Thresholdingmentioning

confidence: 99%

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Toz

Erdoğmuş

2021

IEEE Access

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“…For example, given an original sequence of interval values being [2, I Note: In actual implementation, a total of 11 bits would be required, i.e., 10 bits of Huffman codeword, plus 1 flag bit that serves as the pointer to the run-lengths in the modified sequence of block symbols. 1, 1, 1, 1, 5], the modified sequence is [2,1,5], where the '1' in the middle is indeed a pointer to an entry in the sequence of run-lengths as described previously in subsection A. The entry being pointed to has a value of 4, meaning four consecutive intervals with identical value being 1 in the original sequence of interval values.…”

Section: Generation Of the Modified Sequence Of Interval Valuesmentioning

confidence: 99%

“…Although high resolution grayscale images might be useful for human eyes, binary images might be more suitable for some applications, where color or grayscale images may be converted to binary images. Examples include visual sensor networks [1], object detection [2], edge detection [3], morphological operation [4], Region of Interest (ROI) segmentation [5] among many others.…”

Section: Introductionmentioning

confidence: 99%

Improved distance coding of binary images by run length coding of the most probable interval

Liaghati¹,

Pan

2015

SoutheastCon 2015

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We proposed a new method to improve our previous work on efficient distance-coding of binary images, where we compressed a binary image by applying the bzip2 lossless data compressor on a sequence of intervals, which represent the distances between identical source symbols -either zeros or ones for binary images. Motivated by the observation that a majority of intervals tends to be one, we propose to run-length code this most probable interval independently from the rest of the intervals. Separate Huffman coding tables were used to code the run-lengths of the most probable interval versus other intervals.Consequently, this hybrid coding scheme allows a fraction of one bit to be assigned to the most probable interval on average, as opposed to at least one bit per interval without run-length coding, thereby contributing to about 17% improvement on the compression ratios on some test images.

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“…The goal of a computer-aided diagnosis (CAD) system is to peruse mammograms, find the suspicious areas of irregularities and evaluate their attributes. The efficiency and integrity of a CAD depend upon accurate segmentation of the lesions as well as proper feature selection [29]. The quality of the images, indeed, has a significant influence on the performance of any CAD system.…”

Section: Introductionmentioning

confidence: 99%

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

Abdulla

Sagheer

Veisi

2021

mendel

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Computer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.

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ROI segmentation using Local Binary Image

Cited by 11 publications

References 11 publications

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Improved distance coding of binary images by run length coding of the most probable interval

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

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