Computerized Segmentation Method for Individual Calcifications Within Clustered Microcalcifications While Maintaining Their Shapes on Magnification Mammograms

Hizukuri, Akiyoshi; Nakayama, Ryohei; Nakako, Nobuo; Kawanaka, Hiroharu; Takase, Haruhiko; Yamamoto, Kōji; Tsuruoka, Shuichi

doi:10.1007/s10278-011-9420-z

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…There are number of algorithms being proposed in the field of medical image segmentation [7]. These techniques are broadly classified into four categories: methods based on gray level features, methods based on texture features, model-based segmentation methods, and atlas-based segmentation methods [8][9][10][11][12][13].…”

Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…Several approaches have been undertaken to improve the assessment of MC including X-ray magnification views [4, 6, 22–29] and computer-aided detection with partially successful results: the contrast-limited adaptive histogram equalization (CLAHE) technique for example showed promising results for the computer-aided detection in digital mammograms with a sensitivity of 92.54% and a specificity of 92.50% [30]. However, assessment of the real 3D structure of MC remains challenging in 2D projection views.…”

Section: Discussionmentioning

confidence: 99%

A Micro CT Study in Patients with Breast Microcalcifications Using a Mathematical Algorithm to Assess 3D Structure

et al. 2017

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PurposeThe aim of this study was to evaluate the relevance of the three-dimensional (3D) structure of breast microcalcifications (MC) as a predictor of malignancy using highly resolved micro-computed tomography (micro-CT) datasets of biopsy samples.Material and MethodsThe study included 28 women with suspicious MC in their mammogram undergoing vacuum-assisted biopsy. Directly after the intervention, the specimens were scanned in a micro-CT with an isometric spatial resolution of 9 μm. Datasets were analysed regarding the number, volume and morphology of suspicious non-monomorphic MC (fl—fine linear, fp—fine pleomorphic, ch—coarse heterogeneous) and the structure model index (SMI). Histological evaluation was performed according to the B-classification: normal tissue or benign (group A: B1, B2), unclear malignant potential or suspicious of malignancy (group B: B3, B4) and malignant lesions (group C: B5).ResultsIn all groups, suspicious non-monomorphic MC were found: group A exhibited fp MC in 38.5% of samples, no fl/ch; group B: fl 14.3%, fp 28.6%, ch 14.3%; group C always had at least one type of suspicious non-monomorphic MC (fl (57.1%) or fp (57.1%)) in each sample. The different histologic groups showed a similar mean SMI (benign: 2.97 ± 0.31, malignant: 3.02 ± 0.10, unclear: 2.90 ± 0.28). Between the three groups, no significant differences were found regarding number, volume or SMI value of MC.Conclusion3D structure based on the SMI of MC analysed with highest spatial resolution is not significantly associated with the B-classification of breast lesions. Thus, magnification views of MC may be omitted in the analysis of MC detected in mammograms.

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“…Hizukuri et al [140] proposed a computerised segmentation method for mammogram calcifications and maintained their shapes in the CADx schemes. The method was evaluated using 96 mammogram images acquired from the Breastopia Namba Hospital, Miyazaki, Japan (BNHMJ).…”

Section: Mammogram Breast Cancer Segmentation Based Onmentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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Computerized Segmentation Method for Individual Calcifications Within Clustered Microcalcifications While Maintaining Their Shapes on Magnification Mammograms

Cited by 6 publications

References 25 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

A Micro CT Study in Patients with Breast Microcalcifications Using a Mathematical Algorithm to Assess 3D Structure

Breast Cancer Segmentation Methods: Current Status and Future Potentials

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