Reconstructing 3-D Skin Surface Motion for the DIET Breast Cancer Screening System

Botterill, Tom; Lotz, Thomas; Kashif, Amer Sohail; Chase, J. Geoffrey

doi:10.1109/tmi.2014.2304959

Cited by 27 publications

(21 citation statements)

References 32 publications

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“…Ensemble empirical mode decomposition (EEMD) has been proposed for the prior determination of breast tumors using ultra-wide band (UWB) microwave imaging. Approximately 4 mm tumor has been identified in inside the glandular tissue whose di-electric constant was 35 in a breast model [7]. The pulsed confocal approach was proposed to improve the identification of breast tumors.…”

Section: A Existing Methods For the Identification Of Malignantmentioning

confidence: 99%

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Talha¹,

Kushsairy²,

Shahzad³

et al. 2020

IJACSA

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Breast cancer and heart disease can be acknowledged as very dangerous and common disease in many countries including Pakistan. In this paper classifiers comparative study has been performed for the tumor and heart disease classification. Around one lac women are diagnosed annually with this life-threatening disease having no family history of the disease. If it is not treated on time it may grow and spread to the other parts of human body. Mammograms are the X-rays of the breast which can be used for the screening of cancer tumor. Prior identification of breast cancer may increase the chance of survival up to 70 percent. Tumors which causes cancer can be categorized into two types: a) Benign and b) Malignant. Benign tumor can be explained as the tumor which are not attached to neighbor tissues or spread in the other parts of the body. In Malignant tumor, other parts may be affected by it as it can grow and spread in the other parts of the body. To classify the tumor as Malignant or Benign is very complex as the similarities of cancer tumor and tumor caused by the skin inflammation are almost same. The early identification of Malignant is mandatory to protect the patient life. Diversified medical methods based on deep learning and machine learning have been developed to treat the patients as cancer is a very serious and crucial issue in this era. In this research paper machine learning algorithms like logistic regression, K-NN and tree have been applied to the breast cancer data set which has been taken from UCI Machine learning repository. Comparative study of classifiers has been performed to determine the better classifier for the robust prediction of breast tumors. Simulated results proved that using Logistic regression, ninety-one percent accuracy was achieved. The research showed that logistic regression can be applied for the accurate and precise early prediction of breast cancer. Cardiovascular disease is very common throughout the world. It has been noticed that health in cardiac patients that there are so many factors which causes heart disease or heart attack. The factors leading to the heart failure includes varying blood pressure, high sugar, cardiac pain, and heart rate, high cholesterol level (LDL), artery blockage and irregular ECG signals. Many researchers proved that stress in patients can also be the reason for the heart disease. Higher numbers of cardiac surgeries like angioplasty and heart bypass are performed on annual basis. Actually, people don't care about their lifestyle and diet and fully ignore the symbols. It can be early predicted and cured if proper testing and medication for heart is done. Sometimes there is a false pain which has the same feeling like angina pain depicting cardiovascular disease. To reduce the false alarm and robustly classify the heart disease, several machine learning approaches have been adopted. In proposed research for the accurate classification of heart disease comparison has been performed among support vector machine (SVM), K-nearest neighbors K-NN and lin...

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Section: A Existing Methods For the Identification Of Malignantmentioning

confidence: 99%

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Talha¹,

Kushsairy²,

Shahzad³

et al. 2020

IJACSA

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“…An actuator induces a sinusoid motion at the nipple area, and 5 cameras are used to create 3D stereo-images with at least 10 phases with respect to the actuator motion. Modal analysis is one possible diagnostic metric [7], [14] along with direct image processing to note how tumors show less phase difference than normal areas due to the elasticity to detect stiffer tumors. [12], [14] Once a tumor is identified, Finite Element Analysis (FEM) is used to detect the exact 3D position of the tumor.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, noise removal was necessary. [14] In traditional software noise removal methods, there is a trade-off between detail preservation and noise removal. Our target is particularly to detect smaller tumors to diagnose early to save lives, thus multiple frames were used to achieve both detail preservation and noise removal.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement for In-Vivo Breast Cancer Screening Using DIET system

Nazir¹,

Younis²,

Shahzad³

2020

Preprint

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Breast cancer is a leading cause of death among women. Conventional screening methods, such as mammography, and ultrasound diagnosis are expensive and have significant limitations. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening system that has a potential to be a low cost and reliable breast cancer screening tool. It is based on modal analysis of the breast mass, and stereographic 3D image analysis to detect the stiffer abnormal tissues. However, camera sensor noise, especially Gaussian noise is a major source of Optical Flow (OF) error in this approach to tumor detection. This work studies the performance of different conventional filters, including the standard Gaussian filter tool to remove this noise and produce more robust screening results. A radical approach, Multiple Frame Noise Removal (MFNR) is proposed, for use in this type of medical image processing instead of a Gaussian filter or other typical image noise removal tools. Its a multiple frame noise removal method where Probability Density Function (PDF) of noise is extracted from the multiple images by characterizing the same pixel positions in multiple images. The noise becomes deterministic, and hence easily removed. The proposed algorithm was applied to a data set from 10 phantom breast tests with a prototype DIET system, and 10 in-vivo samples from healthy women. Comparisons were made to an optimal Gaussian filter form that is commonly used. Reductions in OF error using these digitally imaged data sets was used to compare performance. Refinement of the images for medical applications requires higher PSNR, which was successfully achieved by using MFNR algorithm. In this study, the algorithm was used to improve the imaging results of a DIET system. The conventional wisdom that states that noise removal and detail preservation are contrasting effects is

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“…While MR elastography induces vibrations in breast by using MR scanner imaging the surface motion, however MR imaging is expensive [5]. So limitation of above methodologies motivate for advancement of DIET system which is an economical and non-invasive modality for screening test of breast cancer [6].…”

Section: Introductionmentioning

confidence: 99%

Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

Hussain

Rehman

Nazir

et al. 2015

2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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Digital Image Elasto Tomography (DIET) is noninvasive approach for detection and screening test of breast cancer at an early stage. In DIET mechanical vibrations are applied on the surface of breast after applying fiducial markers and images are taken to capture surface motion using five digital cameras. Applying Scale Invariant Feature Transform (SIFT) on the unmarked surface of the breast to produce reference points for tracking surface motion. Using Separate Modal Analysis frequency and amplitude response of oscillations are evaluated using markers. SIFT features are used to detect change in stiffness which refers to presence and/or location of tumor. This increases motion data and reduces noise for detection of cancerous tumours.

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Reconstructing 3-D Skin Surface Motion for the DIET Breast Cancer Screening System

Cited by 27 publications

References 32 publications

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Image Enhancement for In-Vivo Breast Cancer Screening Using DIET system

Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

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