Medical image rigid registration using a novel binary feature descriptor and modified affine transform

Yelampalli, Praveen Kumar Reddy; Nayak, Jagadish; Gaidhane, Vilas H.

doi:10.1049/iet-ipr.2017.0526

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Receptive field inhibition curvilinear feature extraction was proposed which was a biologically useful approach. 126 Thick line detection, rotating kernel-based linear feature detection, structure tensor and phase congruency-based curvilinear structure detection, and Gabor-based feature detection were proposed in the literature. Curvilinear features can be extracted by an initial band pass filtering followed by log-Gabor filter for good DC responses, and no limit setting for maximum bandwidth.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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“…However, STFT uses fixed window size for which there is a low time–frequency resolution. Therefore, several filters including different wavelet filters have been proposed to provide space‐frequency signal decomposition [11–19].…”

Section: Gabor Wavelet Transformmentioning

confidence: 99%

“…Same authors used a binary feature descriptor to discriminate normal and abnormal chest computed tomography (CT) images [12]. In their next method [13], the registration of medical images is based on binary feature descriptor and modified affine transform. A local wavelet pattern for the retrieval of CT images is discussed in [14].…”

Section: Introductionmentioning

confidence: 99%

New design of adaptive Gabor wavelet filter bank for medical image retrieval

Samantaray

Rahulkar

2020

IET Image Processing

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“…Further, a locally linear transformation function is used for robust feature matching in the Hilbert space [28]. Furthermore, Yelampalli et al [29] presented a low‐dimensional binary feature extraction algorithm for medical image rigid registration. A modified affine transformation is formulated to efficiently align ultrasound, CT, and MR images and exhaustively tested in rotation and noisy environments.…”

Section: Introductionmentioning

confidence: 99%

“…Further, these registered images are fused and the fusion performance is verified in terms of entropy [30], standard deviation (SD) [31], edge strength [32], sharpness [33], and average gradient (AG) [31]. The results are compared with the existing popular methods such as local binary pattern (LBP) [19], local tetra pattern (LTrP) [34], local diagonal extrema pattern (LDEP) [35], and local diagonal Laplacian pattern (LDLP) [29].…”

Section: Introductionmentioning

confidence: 99%

Daubechies wavelet‐based local feature descriptor for multimodal medical image registration

2018

Self Cite

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A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining andencoding the Daubechies wavelet decomposed center–neighbour pixel relationshipin the local texture. RDbW features are applied in spatial alignment(registration) of multimodal medical images using a Procrustes analysis(PA)‐based affine transformation function and the registered images are furtherfused by employing a wavelet‐based fusion method. A significant amount ofexperiments is conducted and the registration and fusion accuracy of theproposed feature descriptor is compared with the prominent existing methods suchas local binary patterns (LBP), local tetra pattern (LTrP), local diagonalextrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP).Experimental results show the present registration method improves the averageregistration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP,and LBP, respectively. Further, the fusion results of the current approachexhibit an average improvement in entropy by 11%, standard deviation by 6% edgestrength by 12%, sharpness by 23%, and average gradient by 16% when comparedwith all other feature descriptors used for registering the images. Conceptspresented here can be used widely in analysing the combined information presentin multimodal medical images.

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Medical image rigid registration using a novel binary feature descriptor and modified affine transform

Cited by 8 publications

References 26 publications

Carotid artery ultrasound image analysis: A review of the literature

Carotid artery ultrasound image analysis: A review of the literature

New design of adaptive Gabor wavelet filter bank for medical image retrieval

Daubechies wavelet‐based local feature descriptor for multimodal medical image registration

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