The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

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Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.

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IntOPMICM: Intelligent Medical Image Size Reduction Model

Brain tumor is a life-threatening disease with fast growth rate, which makes its detection a critical task. However, low contrast and noise content in brain magnetic resonance images (MRI) hampers the screening of brain tumor. Therefore, contrast enhancement of these images are necessary to obtain a more definitive imaging for tumor detection. This paper presents an optimized enhancement model for processing Brain MRI by employing morphological filters in coherence with human visual system (HVS) system. The HVS coherence in response of filtering process is incorporated by combination of top-hat and bottom-hat morphological operators using logarithmic image processing model. Application of morphological filter requires selection of structuring element of requisite shape and size to ensure precision in brain tumor detection. This process is challenging as brain tumors (in MRI) may vary rigorously in size and morphology with each case or stages of tumor. Herein, this constraint has been resolved by using a disk-shaped structuring element whose order (size) is optimized using particle swarm optimization algorithm. The enhancement results are quantitatively evaluated using image quality measurement parameters like contrast improvement index, average signal to noise ratio, peak signal to noise ratio and measure of enhancement.

Human visual system based optimized mathematical morphology approach for enhancement of brain MR images

Versatile Approaches for Medical Image Compression: A Review

Agriculture is the backbone of Indian economy. Diseases in crops are causing huge loss to the economy. Only early detection can reduce these losses. Manual detection of the diseases is not feasible. Automated detection of plants diseases using image processing techniques would help farmers in earlier detection and thus prevent huge losses. Maize is an important commercial cereal crop of the world. The aim of this study is the detection of common fungal diseases, common rust, and northern leaf blight in maize leaf. The proposed system aims at early detection and further classification of diseases into common rust, northern leaf blight, multiple diseases, or healthy using first-order histogram features and Haar wavelet features based on GLCM features. Two classifiers, namely, k-NN and SVM are considered for the study. The highest accuracy of 85% is obtained with k-NN for k = 5 and accuracy obtained with SVM-based classification is 88%.

Fungal Disease Detection in Maize Leaves Using Haar Wavelet Features

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely DIR and non-DIR portions, using a graph-based segmentation procedure. The prediction process is implemented using two identical feed-forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed in both a lossless (LLP) and near-lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model-based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It is found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods.

Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

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Lossy image compression based on prediction error and vector quantisation

Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

Deep learning model for deep fake face recognition and detection

In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%.

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An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%. 

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Mohamed Uvaze Ahamed Ayoobkhan

Papers

Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

Lossy image compression based on prediction error and vector quantisation

Deep learning model for deep fake face recognition and detection

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

Web page recommendation system by integrating ontology and stemming algorithm