Steerable Texture Descriptor for an Effective Content-Based Medical Image Retrieval System Using PCA

Jyothi, B.; MadhaveeLatha, Y.; Mohan, P. G. Krishna; Reddy, V. S. K.

doi:10.1007/978-81-322-2517-1_29

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…For designing CBIR in medical images Steerable filter is used for extracting feature of texture and for representing features of texture by using PCA technique. For retrieving the similar images from the large data set using Euclidean distance [27].For the segmentation of tumor tissues in the MRI Brain image using FCM and K-means clustering. In the performance analysis K-means clustering performed better result [28].To segment the glioma tumor image, for the training process cascaded CNN architecture was implemented using patches [29].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI

Venkatachalam

Siuly

Bačanin³

et al. 2021

IEEE Access

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Brain tumors are a serious and death-defying disease for human life. Discovering an appropriate brain tumor image from a magnetic resonance imaging (MRI) archive is a challenging job for the radiologist. Most search engines retrieve images on the basis of traditional text-based approaches. The main challenge in the MRI image analysis is that low-level visual information captured by the MRI machine and the high-level information identified by the assessor. This semantic gap is addressed in this study by designing a new feature extraction technique. In this paper, we introduce Content-Based Medical Image retrieval (CBMIR) system for retrieval of brain tumor images from the large data. Firstly, we remove noise from MRI images employing several filtering techniques. Afterward, we design a feature extraction scheme combining Gabor filtering technique (which is mainly focused on specific frequency content at the image region) and Walsh-Hadamard transform (WHT) (conquer technique for easy configuration of image) for discovering representative features from MRI images. After that, for retrieving the accurate and reliable image, we employ Fuzzy C-Means clustering Minkowski distance metric that can evaluate the similarity between the query image and database images. The proposed methodology design was tested on a publicly available brain tumor MRI image database. The experimental results demonstrate that our proposed approach outperforms most of the existing techniques like Gabor, wavelet, and Hough transform in detecting brain tumors and also take less time. The proposed approach will be beneficial for radiologists and also for technologists to build an automatic decision support system that will produce reproducible and objective results with high accuracy.

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Section: Related Workmentioning

confidence: 99%

An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI

Venkatachalam

Siuly

Bačanin³

et al. 2021

IEEE Access

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“…The method is to extract the low-level features on the example image firstly, to compute the similarity between the query image and the images in image database secondly, to sort images by similarity measure lastly, and then the top images will be displayed. CBIR is more effective and subjective than text-based apparently, but fails to describe semantic concepts elaborately, so many researches proposed some methods in order to reduce the semantic gap between content-based and semantic-based [4], one of these approaches is fuzzy quantization based that is closer to human perception [2,8,14]. The fuzzy theory is the basis of artificial intelligence, so color space can be quantized by fuzzy membership degree function, which is more coordinately with human perception.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Vehicle Images based on Color Space Fuzzy Quantification in Criminal Investigation

Mingdi¹

2017

IJPE

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______________________________________________________________________________________________________________ AbstractThe color and shape features provide more contributions for vehicle images in criminal investigation generally. A criminal investigation library that was used in studying criminal tools was set up, in which the number of the car images is more than one thousand. In this paper, the color of the car pictures was quantized by the non-uniform quantization, the triangular and the trapezoidal fuzzy membership degree functions respectively at the first step, and then the Euclidean distance and the weighted distance similarity measures methods are used at the second step, the last step is that the car images were retrieved by the above six algorithms in the criminal investigation library. The results show that the weighted distance similarity measure algorithm based trapezoidal membership degree is better than others; meanwhile the precision and recall are significantly higher than other else. The triangular fuzzy quantization algorithm is not very different from the non-uniform quantization algorithm, and even the precision and recall ratio is smaller than the latter. Under the same quantization condition, the weighted distance algorithm is slightly better than the Euclidean distance, but the difference is not great.

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“…Recently, CBIR has been successfully implemented in different fields, such as medical diagnosis, 3 geographic information 4 and remote sensing, 5 trademark and intellectual property, 6 and so on. The methods of CBIR include texture-based, 7,8 color-based, 9 shape-based, 10 multi features fusion, 11 and deep learning. 12 In the textile and garment industry, research into CBIR mainly focuses on patterned fabric, including fabric patterns, 13,14 printed fabric, 15,16 Jacquard fabric, 17 and lace fabric.…”

mentioning

confidence: 99%

Image retrieval of wool fabric. Part I: Based on low-level texture features

Zhang

Xiang

Wang

et al. 2019

Textile Research Journal

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With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

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Steerable Texture Descriptor for an Effective Content-Based Medical Image Retrieval System Using PCA

Cited by 4 publications

References 8 publications

An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI

An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI

Retrieval of Vehicle Images based on Color Space Fuzzy Quantification in Criminal Investigation

Image retrieval of wool fabric. Part I: Based on low-level texture features

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