Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.

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A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/743870 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Huiyan Jiang ◽

Baochun He ◽

Di Fang ◽

Zhiyuan Ma ◽

Benqiang Yang ◽

...

Keyword(s):

Region Of Interest ◽

Region Growing ◽

Vessel Segmentation ◽

Growth Strategy ◽

Segmentation Algorithm ◽

Edge Information ◽

Manual Intervention ◽

Vascular Structures ◽

Region Growing Segmentation ◽

Seed Points

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure’s center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.

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An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.11801 ◽

2016 ◽

Vol 6 (6) ◽

pp. 2773 ◽

Cited By ~ 35

Author(s):

Sirshendu Hore ◽

Souvik Chakraborty ◽

Sankhadeep Chatterjee ◽

Nilanjan Dey ◽

Amira S. Ashour ◽

...

Keyword(s):

Image Segmentation ◽

Region Of Interest ◽

Region Growing ◽

Threshold Value ◽

Pixel Intensity ◽

Integrated Method ◽

Seeded Region Growing ◽

Segmented Images ◽

Otsu’S Method ◽

Region Growing Segmentation

<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

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An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.pp2773-2780 ◽

2016 ◽

Vol 6 (6) ◽

pp. 2773 ◽

Cited By ~ 3

Author(s):

Sirshendu Hore ◽

Souvik Chakraborty ◽

Sankhadeep Chatterjee ◽

Nilanjan Dey ◽

Amira S. Ashour ◽

...

Keyword(s):

Image Segmentation ◽

Region Of Interest ◽

Region Growing ◽

Threshold Value ◽

Pixel Intensity ◽

Integrated Method ◽

Seeded Region Growing ◽

Segmented Images ◽

Otsu’S Method ◽

Region Growing Segmentation

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Towards a Support System for Digital Mammogram Classification

Studia Universitatis Babeș-Bolyai Informatica ◽

10.24193/subbi.2021.2.02 ◽

2021 ◽

Vol 66 (2) ◽

pp. 19

Author(s):

A. Bajcsi

Keyword(s):

Feature Selection ◽

Nearest Neighbor ◽

Early Stage ◽

Region Of Interest ◽

Principal Component ◽

Region Growing ◽

Pectoral Muscle ◽

Support Vector ◽

K Nearest Neighbor ◽

Similar System

Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.

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A NOVEL APPROACH FOR BRAIN TUMOR DETECTION USING DW-MTM FILTER AND REGION GROWING SEGMENTATION IN MR IMAGING

10.21506/j.ponte.2018.3.10 ◽

2018 ◽

Vol 74 (3) ◽

Author(s):

Suneetha Bobbillapati ◽

A. Jhansi Rani

Keyword(s):

Brain Tumor ◽

Mr Imaging ◽

Region Growing ◽

Tumor Detection ◽

Novel Approach ◽

Region Growing Segmentation

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Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020969502 ◽

2020 ◽

pp. 095440702096950

Author(s):

Bochang Zou ◽

Huadong Qiu ◽

Yufeng Lu

Keyword(s):

Convex Hull ◽

Target Object ◽

Identification Accuracy ◽

Detection Accuracy ◽

Morphological Operations ◽

Circle Detection ◽

Spherical Object ◽

Single Feature ◽

Contour Fitting ◽

Rgb Images

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.

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GUI-CAD Tool for Segmentation and Classification of Abnormalities in Lung CT Image

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2019010102 ◽

2019 ◽

Vol 8 (1) ◽

pp. 9-31

Author(s):

V. Vijaya Kishore ◽

R.V.S. Satyanarayana

Keyword(s):

Region Of Interest ◽

Performance Measure ◽

Morphological Operations ◽

Interactive 3D ◽

And Performance ◽

Aided Diagnosis ◽

Lung Ct ◽

Clinical Expert ◽

Clinical Determination

A vital necessity for clinical determination and treatment is an opportunity to prepare a procedure that is universally adaptable. Computer aided diagnosis (CAD) of various medical conditions has seen a tremendous growth in recent years. The frameworks combined with expanding capacity, the coliseum of CAD is touching new spaces. The goal of proposed work is to build an easy to understand multifunctional GUI Device for CAD that performs intelligent preparing of lung CT images. Functions implemented are to achieve region of interest (ROI) segmentation for nodule detection. The nodule extraction from ROI is implemented by morphological operations, reducing the complexity and making the system suitable for real-time applications. In addition, an interactive 3D viewer and performance measure tool that quantifies and measures the nodules is integrated. The results are validated through clinical expert. This serves as a foundation to determine, the decision of treatment and the prospect of recovery.

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