ArticleBMC cancer2018
Removal of pectoral muscle based on topographic map and shape-shifting silhouette.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 88 citations in OpenAlex.
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- Dynamic kernel generation through hybrid involution and convolution neural networks for leukemia and white blood cell classification.Scientific reports · 2025Article
- Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images.Journal of imaging · 2024Article
- Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.EJNMMI reports · 2024Review
- Comparison between two packages for pectoral muscle removal on mammographic images.La Radiologia medica · 2022Article
- Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System.Computational intelligence and neuroscience · 2022Article
- The Effect of Breast Size and Density in Turkish Women on Radiation Dose in Full-Field Digital Mammography.European journal of breast health · 2021Article
- Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.Microscopy research and technique · 2021Article
- Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons.IT professional · 2021Article
- Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM).Environmental technology & innovation · 2021Article
- Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.Journal of medical systems · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn digital mammography, finding accurate breast profile segmentation of women's mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing.
methodsWe propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images.
resultsTo assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth.
conclusionsOur approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.