scholarly journals Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8265
Author(s):  
Ana Catarina Pelicano ◽  
Maria C. T. Gonçalves ◽  
Daniela M. Godinho ◽  
Tiago Castela ◽  
M. Lurdes Orvalho ◽  
...  
Keyword(s):  
Experimental Testing ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Bias Field ◽  
Breast Cancer Diagnosis ◽  
Microwave Imaging ◽  
Physical Models ◽  
Malignant Tumours ◽  
Tumour Segmentation ◽  
Magnetic Resonance Imaging Mri

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

ADAPTIVE REGION GROWING FOR SKULL, BRAIN, AND SCALP SEGMENTATION FROM 3D MRI

2019 ◽  
Vol 31 (05) ◽  
pp. 1950033
Author(s):  
Tran Anh Tuan ◽  
Jin Young Kim ◽  
Pham The Bao
Keyword(s):  
Nervous System ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Ground Truth ◽  
Resonance Imaging ◽  
3D Mri ◽  
Histogram Method ◽  
Ground Truth Data ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Many of the diseases that are related to the nervous system are serious and can lead to pain, headaches, and many other symptoms. Magnetic resonance imaging (MRI), which produces detailed medical images for the diagnosis of nervous system-related diseases, offers an advanced approach to the study of these diseases. In this paper, a method for the automatic segmentation of the skull, scalp, and brain of 3D MRI imagery is proposed to help doctors diagnose numerous injuries and diseases. For the proposed method, the 3D MRI dataset was first transformed to 2D axial slices, which were grouped based on the skull shape. Second, the histogram method was used to compute the fitting rectangle boundary around the object. Third, the segmentation of the skull was performed using the proposed adaptive region growing method, depending on the slice groups. Finally, the brain and the scalp were segmented. The proposed method was tested and validated on 20 subjects from the BrainWeb database and seven adults from the Neurodevelopmental MRI database with the use of the tissues for the ground truth data. The performance of the proposed method was evaluated using the Dice percentage and a comparison with the other methods.

Breast Cancer Image Classification: A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Pooja Pathak ◽  
Anand Singh Jalal ◽  
Ritu Rai
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Treatment Options ◽  
Breast Cancer Diagnosis ◽  
Conventional Approach ◽  
Learning Networks ◽  
Cad System ◽  
Ultrasound Computed Tomography ◽  
Core Concepts ◽  
Magnetic Resonance Imaging Mri

Background: Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer such as mammogram, ultrasound, computed tomography, Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. Objective: This paper aims to cover the approaches used in CAD system for the detection of breast cancer. Method: In this paper, the methods used in CAD systems are categories in two classes: the conventional approach and artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. Conclusion: This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment

2021 ◽  
Vol 11 (8) ◽  
pp. 3606
Author(s):  
Seonho Lim ◽  
Young Joong Yoon
Keyword(s):  
Breast Cancer ◽  
Coupling Effect ◽  
Breast Cancer Diagnosis ◽  
Microwave Imaging ◽  
Ultra Wideband ◽  
Slot Antenna ◽  
Pin Diode ◽  
Time Frequency ◽  
Switching Mode ◽  
Meander Line

In this paper, a wideband-narrowband switchable tapered slot antenna (TSA) with a compact meander line resonator for an integrated microwave imaging and hyperthermia system was proposed. A compact meander line resonator, which exhibited band-pass characteristics and provided narrowband characteristics by using one PIN diode, was fabricated beneath the tapered slot of the wideband TSA to minimize the degradation of the wideband characteristics. Moreover, the electromagnetic energy was transferred to the meander line resonator with a coupling effect to ensure effective frequency switching. By adapting a PIN diode on the meander line resonator, frequency switching could be achieved. In this way, the proposed antenna could operate in a real-time frequency switching mode between the ultra-wideband (UWB; 3.1~10 GHz), which is used for microwave imaging, and the 2.45 GHz band (industrial, scientific, and medical, ISM band), which is used for microwave hyperthermia. Frequency and time-domain results proved the applicability of the proposed antenna to an integrated breast cancer detection and treatment system.

MRI features in a malignant glomus jugulare tumour

1993 ◽  
Vol 107 (11) ◽  
pp. 1066-1069 ◽  
Author(s):  
Alexandra C. Athanassopoulou ◽  
Labros L. Vlahos ◽  
Athanassios D. Gouliamos ◽  
Eliana D. Kailidou ◽  
John G. Papailiou ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Pulmonary Metastases ◽  
Distant Metastases ◽  
Resonance Imaging ◽  
Malignant Tumours ◽  
Regional Lymph Nodes ◽  
Glomus Jugulare ◽  
Mri Features ◽  
Magnetic Resonance Imaging Mri ◽  
Glomus Jugulare Tumour

AbstractMagnetic resonance imaging (MRI) features in a case of malignant glomus jugulare tumour are reported. Chemodectomas are benign in 95 per cent of cases and malignant in five per cent. Only one case report of CT findings in this unusual CP angle tumour with pulmonary metastases has been cited in the literature.It is concluded that MRI can provide useful information about the nature of chemodectomas although it cannot dislinguish between benign and malignant tumours, except when regional lymph nodes are involved or when distant metastases exist.

scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

scholarly journals A Research and Application of the Processing Methods of Railway Measurement Based on Self-Mobile Scanning Measurement System

2020 ◽  
Author(s):  
Jiancai Zhang ◽  
Hang Mu ◽  
Feng Han ◽  
Shumin Han
Keyword(s):  
Measurement System ◽  
High Speed ◽  
Automatic Segmentation ◽  
Laser Scanner ◽  
Region Growing ◽  
Image Sensor ◽  
Operating Conditions ◽  
Secondary Development ◽  
Railway Line ◽  
Region Growing Segmentation

With the gradual improvement of China’s railway net, the opening of international railways as well as the continuous growth of railway operating mileage, the workload of remeasuring railways is increasing. The traditional methods of remeasuring railways can not meet current high-speed and high-density operating conditions anymore in terms of safety, efficiency and quality, so a safer and more efficient measurement method is urgently needed.This thesis integrated various sensors on a self-mobile instrument, such as 3D laser scanner, digital image sensor and GNSS_IMU, designing a set of intelligent and integrated self-mobile scanning measurement system. This thesis proposed region growing segmentation based on the reflection intensity of point cloud. Through the secondary development of CAD, the menu for automatic processing of self-mobile scanning measurement system is designed to realize rail automatic segmentation, extraction of rail top points, fitting of plane parameters of railway line, calculation of curve elements and mileage management.The results show that self-mobile scanning measurement system overcomes the shortcomings of traditional railway measurement to some extent, and realizes intelligent measurement of railways.

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