A Novel Content Based Medical Image Retrieval Technique with Aid of Modified Fuzzy C-Means Clustering (CBMIR-MFCM)

Malliga, L.; Raja, K B

doi:10.1166/jmihi.2016.1734

Cited by 4 publications

(2 citation statements)

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“…The mathematical framework of the approach is defined clearly. Malliga et al [17] presented the Modified Fuzzy C Means clustering technique based Content Based Medical Image Retrieval (CBMIR) system. In this system, initially, the Haralick and Texture spectrum features are extracted from medical image database.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning-based Content-based Image Retrieval Framework

Sivakumar¹,

Kumar²,

Karthikeyan³

2022

Computer Systems Science and Engineering

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The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c-Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

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Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning-based Content-based Image Retrieval Framework

Sivakumar¹,

Kumar²,

Karthikeyan³

2022

Computer Systems Science and Engineering

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“…When high values are obtained according to the evaluation, it is indicated that the distance between the characterizations of the images is high and, therefore, that the similarity between them is low. The formula for calculating the Euclidean distance in an n-dimensional space would be [19]:…”

Section: Similarity Measuresmentioning

confidence: 99%

CBIR System Using Scaled Conjugate Gradient Feed-Forward Neural Network

2020

IJATCSE

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Images produced by copious of application has now become the centre of attraction for researchers. The task of retrieval of desired image from the ocean of images is quite tedious task. The paper focuses on mining of database from the picture descriptions that are recovered from the same picture known as Content Based Image Retrieval (CBIR). In this paper, we have used colour, texture and shape picture description and Scaled Conjugate Gradient feed-forward neural network (SCG-FFNN). Initially we have prepared the model using colour histogram for colour feature, wavelet moments for texture feature and edge-histogram and edgedirection for shape features; then trains the dataset using FFNN. The trained database is tested with a picture, and generates the like pictures from it. The execution of the system is calculated with the help of precision and recall parameter and the outcomes are found to be high as compare to other research effort. The practical work has been conducted with Corel and Caltech dataset.

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