Segmentation and Enhancement of Mammograms for the Detection of Cancer Using Gradient Weight Map and Decorrelation Stretch

Divyashree, B. V.; Kumar, G. Hemantha

doi:10.1007/978-981-15-8697-2_34

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…This adoption of deep learning enhances segmentation precision and optimizes computational demands. Additionally, employing the FREAK descriptor in feature extraction replaces the Histogram of Oriented Gradients (HOG) descriptor, aiming to accelerate the process without compromising accuracy [39]. Moreover, refining the matching process by omitting certain verification methods from the previous approach increases efficiency, allowing for faster registration while preserving potential matches.…”

Section: Flowchart Descriptionmentioning

confidence: 99%

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Ochoa-Astorga,

WANG,

et al. 2024

Preprint

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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

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Section: Flowchart Descriptionmentioning

confidence: 99%

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Ochoa-Astorga,

WANG,

et al. 2024

Preprint

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“…A certain number of digital images in dataset contain different artifacts similar to dense regions in the breast. In our previous work, to obtain the precise region of interest (skin-air boundary), gradient weight map method is adopted [30]. The weights are computed for each pixel based on gradient magnitude using 3 × 3 windows.…”

Section: Gradient Weight-based Segmentationmentioning

confidence: 99%

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Divyashree

Kumar

2021

SN COMPUT. SCI.

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Breast cancer is a deadly and one of the most prevalent cancers in women across the globe. Mammography is widely used imaging modality for diagnosis and screening of breast cancer. Segmentation of breast region and mass detection are crucial steps in automatic breast cancer detection. Due to the non-uniform distribution of various tissues, it is a challenging task to analyze mammographic images with high accuracy. In this paper, background suppression and pectoral muscle removal are performed using gradient weight map followed by gray difference weight and fast marching method. Enhancement of breast region is performed using contrast limited adaptive histogram equalization (CLAHE) and de-correlation stretch. Detection of breast masses is accomplished by gray difference weight and maximally stable external regions (MSER) detector. Experimentation on Mammographic Image Analysis Society (MIAS) and curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) show that the method proposed performs breast boundary segmentation and mass detection with best accuracies. Mass detection achieved high accuracies of about 97.64% and 94.66% for MIAS and CBIS-DDSM dataset, respectively. The method is simple, robust, less affected to noise, density, shape and size which could provide reasonable support for mammographic analysis.

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Segmentation and Enhancement of Mammograms for the Detection of Cancer Using Gradient Weight Map and Decorrelation Stretch

Cited by 2 publications

References 11 publications

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

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