Detection of Tumor Using Gabor Filter for Multimodal Images

2020 ◽  
Vol 17 (9) ◽  
pp. 4325-4330
Author(s):  
M. D. Nandeesh ◽  
M. Meenakshi
Keyword(s):  
Gabor Filter ◽  
Principal Component ◽  
Hybrid Technique ◽  
Fusion Algorithm ◽  
Maximum Information ◽  
Magnetic Resonance Imaging Mri ◽  
Multimodal Images ◽  
Hybrid Fusion ◽  
Image Performance ◽  
Ct And Mri

Imaging segmentation techniques play a significant factor in medical justification for diagnosis and therapy application in healthcare industries. These noninvasive procedures assist the physician to visualize the vital part of the human body planned for treatment. Multimodal fused images from Computer tomography (CT) and Magnetic resonance imaging (MRI) provides prominent results in detection of the tumor. Maximum information about the image cannot be obtained from individual technique to assess the location and its dimension of tumor. A fusion of multimodal images like MRI and CT images are used to complimentary information and its segmentation to detect the presence or absence of tumor using objective method. In this paper fusion of CT and MRI is done by a hybrid technique by combining Principal Component Analysis (PCA) and Curvelet Transformation (CVT). Gabor filter based segmentation of this image is applied as post-processing to obtain the presence of exact location of tumor in the image. Performance of fusion and segmentation is analyzed to obtain better quality image. The simulation consequence has shown better images using a hybrid fusion algorithm and Gabor filter is used for assisting the physician to find the presence or absence of tumor. Proposed approach based on simulation results has shown a better efficiency as compared to individual techniques.

A New Adaptive Image Fusion Technique of CT and MRI Images Based on Dual-Tree Complex Wavelet Transform

2013 ◽  
Vol 411-414 ◽  
pp. 1189-1192 ◽  
Author(s):  
Ling Yan Du ◽  
Yin Jie ◽  
Zhan Xu
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Visual Quality ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Image Fusion Technique ◽  
Ct And Mri ◽  
Better Than

In this paper, a new adaptive images fusion algorithm is presented for CT and MRI based on DT-CWT. For fusion, all the source images are decomposed into low and high frequency sub-bands, and then the fusion of low frequency is done by means of Principal Component Analysis (PCA) while for high frequency regional energy algorithm is used. Experiment are carried out on a number of CT and MRT images, results show that the DT-CWT method is better than that of DWT method in terms of quality measures PSNR, NCC and image visual quality.

scholarly journals Dual Filter based Images Fusion Algorithm for CT and MRI Medical Images

2019 ◽  
Vol 8 (9) ◽  
pp. 2673-2678
Keyword(s):  
Wavelet Transform ◽  
Medical Images ◽  
Input Image ◽  
Fusion Algorithm ◽  
Comparison Results ◽  
Fused Image ◽  
Laplacian Filter ◽  
Magnetic Resonance Imaging Mri ◽  
Approximation Coefficients ◽  
Ct And Mri

A novel image fusion algorithm based on two filters, one is laplacian filter for de-nosing the detailed coefficients and second filter is Guided Filter (GF) used to refine the approximation as well as detailed coefficient for Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) medical images is proposed. Because of using wavelet transform, we obtained approximation coefficient and other three coefficients of CT and MRI images. Now two weight maps are obtained after the process of denoising. Another reason for obtaining two weight maps is because of comparison. Here comparison is done between two approximation coefficient and six detailed coefficients. By using the approximation coefficients and detailed coefficients, GF is designed. Here GF will guide an image corresponding to the weight maps. Here the weight maps are smoothed using GF and this is mainly served as input image. Hence the weighted fusion algorithm will fuse the both CT and MRI images. A pure fused image is obtained only when the CT and MRI images are refined by inverse wavelet transform. From the comparison results, it can observe that the proposed system gives better results compared to existing system. As well as the proposed system will give maximum amount of input in detail manner

CT and MRI imaging of glomus tumors of the temporal bone

2018 ◽  
pp. 26-32
Author(s):  
E. A. Stepanova ◽  
М. V. Vishnyakova ◽  
V. I. Sambulov ◽  
I. Т. Mukhamedov
Keyword(s):  
Magnetic Resonance ◽  
Diagnostic Imaging ◽  
Temporal Bone ◽  
Glomus Tumor ◽  
Magnetic Resonance Tomography ◽  
Mri Imaging ◽  
Glomus Tumors ◽  
Magnetic Resonance Imaging Mri ◽  
Temporal Bone Tumor ◽  
Ct And Mri

Glomus tumor is one of the most common temporal bone tumors. Most of them are benign and locally invasive, some are occasionally able to metastasize and have signs of malignancy. Diagnostic imaging is necessary before treatment. Computer tomography (CT) is traditionally used as a primary method of diagnosis, to recognize changes in the temporal bone. Role of magnetic resonance imaging (MRI) in temporal bone tumor diagnosis is not definitively determined.Purpose. To assess the possibilities of computer and magnetic resonance tomography, to develop an algorithm for the application of diagnostic imaging methods in the diagnosis of glomus tumors of the temporal bone.Material and methods. The article presents the experience of diagnosing 30 patients with glomus tumors.Results. The tympanic form of the glomus tumor was observed in 11 cases (37%), tympano-yugular in 19 cases (63%). CT and MRI data totally coincided in cases of small tumors (type A and B). In the presence of extended forms CT ability of assessing bone invasion, involvement of the internal carotid artery, internal jugular vein, and dural sinuses was lower than the MRI.

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 Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

2021 ◽  
Author(s):  
Martina Pecoraro ◽  
Stefano Cipollari ◽  
Livia Marchitelli ◽  
Emanuele Messina ◽  
Maurizio Del Monte ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Imaging Biomarker ◽  
Cross Sectional ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Sectional Analysis ◽  
Ct And Mri ◽  
Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

Bone-related disorders of the jaw: A clinico-radiological diagnostic algorithm

2021 ◽  
pp. 197140092199896
Author(s):  
Ahmed Abdel Khalek Abdel Razek
Keyword(s):  
Diagnostic Algorithm ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Specific Diagnosis ◽  
Clinical Presentations ◽  
Neoplastic Lesions ◽  
Lesion Extension ◽  
Magnetic Resonance Imaging Mri ◽  
The Relationship ◽  
Ct And Mri

Bone-related disorders of the jaw (BRDJ) include a spectrum of non-neoplastic and neoplastic lesions of the maxillofacial region that have been recently classified into fibro-osseous lesions, giant cell lesions and osseous tumours. The histopathological features of BRDJ can be similar and overlie each other. Imaging is important in order to reach a specific diagnosis. However, the appearance of BRDJ on imaging is non-specific in some cases. Computed tomography (CT) and magnetic resonance imaging (MRI) are used for accurate localisation, characterisation of the tumour matrix, delineation of the lesion extension and establishment of the relation of BRDJ to the surrounding structures. Imaging is usually done to detect the relationship with the adjacent surrounding vital structures and to diagnose aggressive forms, malignant transformation and associated syndromes. The correlation of the demographic findings, the location and the clinical presentations with the imaging features are important for the diagnosis of BRDJ. The proposed clinico-radiological diagnostic algorithm with CT and MRI helps a specific diagnosis to be reached in some cases.

scholarly journals Nonconventional Options for Tumor Localization in Breast and Axillary Lymph Nodes: A Pictorial How-To

2018 ◽  
Vol 8 ◽  
pp. 54 ◽  
Author(s):  
Naziya Samreen ◽  
Christine U Lee ◽  
Asha A Bhatt
Keyword(s):  
Breast Lesion ◽  
Preoperative Localization ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Resonance Imaging ◽  
Node Localization ◽  
Magnetic Resonance Imaging Mri ◽  
Lesion Localization ◽  
Ct And Mri ◽  
Breast Malignancies

Preoperative localization of breast malignancies using traditional ultrasound and digital techniques can be challenging, particularly after neoadjuvant chemotherapy when the target is not conspicuous. The purpose of this paper is to pictorially present nontraditional techniques that have been helpful in preoperative localization before surgery. We will discuss techniques for breast lesion localization using computed tomography (CT) and magnetic resonance imaging (MRI) as well as axillary lymph node localization using tomosynthesis, CT, and MRI.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

scholarly journals Ultrasound Monitoring of Descending Aortic Aneurysms and Dissections in Mice

2020 ◽  
Author(s):  
Hisashi Sawada ◽  
Michael K. Franklin ◽  
Jessica J. Moorleghen ◽  
Deborah A. Howatt ◽  
Masayoshi Kukida ◽  
...  
Keyword(s):  
High Frequency ◽  
Aortic Aneurysms ◽  
Aortic Diseases ◽  
Dorsal Approach ◽  
In Vivo Monitoring ◽  
Ultrasound Monitoring ◽  
Magnetic Resonance Imaging Mri ◽  
High Frequency Ultrasonography ◽  
Ct And Mri

Several modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound, are available to visualize mouse aortas.1-3 CT and MRI enable us to obtain reliable images of the aorta and its branches. However, CT requires vascular contrast and MRI is procedurally complex. Thus, these modalities are used only occasionally for in vivo monitoring of mouse studies. High frequency ultrasonography is a common approach for aortic monitoring in mice.4 The standard ultrasound approach using a para-sternal view can visualize the aortic root, ascending aorta, and aortic arch, while this approach cannot visualize the descending region due to the presence of lungs and ribs. Therefore, the ability to perform in vivo monitoring of descending aortic diseases in mice has been an impediment. This study reports a para-spinal dorsal approach for ultrasound imaging of mouse descending aortas.

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