Gabor wavelet based features for medical image analysis and classification

Buciu, Ioan; Gacsádi, A.

doi:10.1109/isabel.2009.5373659

Cited by 30 publications

(12 citation statements)

References 10 publications

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“…Gabor filters are used for the detection of directional elements in image processing, such as classification and edge detection. In BUS images, these filters are frequently used for prepossessing and speckle reduction .…”

Section: Methodsmentioning

confidence: 99%

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

et al. 2018

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Section: Methodsmentioning

confidence: 99%

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

et al. 2018

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“…As a result, using DWT to extract features would allow obtaining better high frequency features than spectral and Gabor transform. Indeed, the disadvantages of Gabor filter can be avoided if wavelet transform is adopted since it provides a precise analysis of a signal at different scales [14] [15].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the fusion system is not easy to implement and data processing is time consuming. Furthermore, Gabor filters have three major limitations [14] [15]. First, the outputs of Gabor filter banks are not mutually orthogonal; then a significant correlation between texture features may occur.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Tongueprint Textures for Personal Authentication: A Wavelet Approach

Lahmiri¹

2012

JAIT

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In order to verify tongueprint images, three approaches for texture analyses were considered and their performances are compared. They are wavelet transform, Gabor filter, and spectral analysis. In all approaches, six statistical measures are applied to the processed images to extract features. They are the mean, standard deviation, smoothness, third moment, uniformity, and entropy. Finally, k-nearest neighbour algorithm (k-NN) is used to classify tongue textures for verification purposes. The obtained recognition rates show that features extracted from wavelet analysis allow achieving the highest accuracy (92%) among the other approaches. On the other hand, features extracted from spectral images lead to the lowest recognition rate (75%). Features extracted from Gabor filter banks obtained 83%. Therefore, we conclude that wavelet-based features outperform Gabor and spectral-based features employed in the literature.

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“…This is more challenging than classification tasks on natural images. The highly cluttered background and the particularity of medical images pose major challenges to classification accuracy [5] [6].…”

Section: Introductionmentioning

confidence: 99%

ACNET: Attention-based Convolution Network with Additional Discriminative Features for DCM Classification (S)

Luo

Wang²,

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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For dilated cardiomyopathy (DCM) patients, immediate emergency diagnosis and treatment are critical for life saving and later recovery. T1 mapping is a non-invasive and effective diagnostic imaging approach to detect DCM. However, it is a demanding and time-consuming approach. In this paper, we propose an attention-based network structure, which can automatically identify DCM patients in a speedy manner to prioritize their treatment. In the proposed method, we adopt attention modules to generate attention-aware features. Inside each attention module, a bottom-up top-down feed-forward structure is used to unfold the feed-forward and feed-back attention processes into a single feed-forward process. It allows the network to focus more on determining useful information about the current output that is significant in the input data. Moreover, inspired by the residual network idea, we make full use of the characteristics of the original data. Combined residual block, we design down-residual modules for classification tasks. It consists of seven convolution layers and three layers of residual blocks. Our network achieves the most advanced recognition performance on cardiac datasets. We evaluated our approach on CMR(cardiac magnetic resonance) T1 mapping images with lower PSNR(peak signal to noise ratio), and the results demonstrate that our architecture outperforms previous approaches.

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Gabor wavelet based features for medical image analysis and classification

Cited by 30 publications

References 10 publications

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

Recognition of Tongueprint Textures for Personal Authentication: A Wavelet Approach

ACNET: Attention-based Convolution Network with Additional Discriminative Features for DCM Classification (S)

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