Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures.Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations.Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images.Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.Keywords: Ripplet, Directional discrete cosine transform (DDCT), Peak signal to noise ratio, MSE (mean square error), SC (structural content), MD (maximum difference), NCC (normalized cross correlatio

Analysis of Different Filters for Image Despeckling : A Review

Digital image acquisition and processing in clinical diagnosis plays a significant part. Medical images at the time of acquisition can be corrupted via noise. Removal of this noise from images is a challenging problem. The presence of signal dependent noise is referred as speckle which degrades the actual quality of an image. Considering, several techniques have been developed focused on speckle noise reduction. The primary purpose of these techniques was to improve visualization of an image followed by preprocessing step for segmentation, feature extraction and registration. The scope of this paper is to provide an overview of despeckling techniques

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

HoEnTOA: Holoentropy and Taylor Assisted Optimization based Novel Image Quality Enhancement Algorithm for Multi-Focus Image Fusion

875-886In machine vision as well as image processing applications, multi-focus image fusion strategy carries a prominent exposure. Normally, image fusion is a method of merging of information extracted out of two or more than two source images fused to produce a solitary image, which is much more instructive as well as much suitable for computer processing and visual perception. In this research paper authors have devised a novel image quality enhancement algorithm by fusing multi-focus images, in short, termed as HoEnTOA. Initially, contourlet transform is incorporated to both of the input images for generation of four respective sub-bands of each of input image. After converting into sub-bands further holoentropy along with proposed HoEnTOA is introduced to fuse multi-focus images. Here, the developed HoEnTOA is integration of Taylor series with ASSCA. After fusion, the inverse contourlet transform is incorporated for obtaining last fused image. Thus, the proposed HoEnTOA effectively performs the image fusion and has demonstrated better performance utilizing the five metrics i.e. Root Mean Square Error with a minimum value of 3.687, highest universal quality index value of 0.984, maximum Peak Signal to Noise Ratio of 42.08dB, maximal structural similarity index measurement of 0.943, as well as maximum mutual information of 1.651

HoEnTOA: Holoentropy and Taylor Assisted Optimization based Novel Image Quality Enhancement Algorithm for Multi-Focus Image Fusion

In machine vision as well as image processing applications, multi-focus image fusion strategy carries a prominent exposure. Normally, image fusion is a method of merging of information extracted out of two or more than two source images fused to produce a solitary image, which is much more instructive as well as much suitable for computer processing and visual perception. In this research paper authors have devised a novel image quality enhancement algorithm by fusing multi-focus images, in short, termed as HoEnTOA. Initially, contourlet transform is incorporated to both of the input images for generation of four respective sub-bands of each of input image. After converting into sub-bands further holoentropy along with proposed HoEnTOA is introduced to fuse multi-focus images. Here, the developed HoEnTOA is integration of Taylor series with ASSCA. After fusion, the inverse contourlet transform is incorporated for obtaining last fused image. Thus, the proposed HoEnTOA effectively performs the image fusion and has demonstrated better performance utilizing the five metrics i.e. Root Mean Square Error with a minimum value of 3.687, highest universal quality index value of 0.984, maximum Peak Signal to Noise Ratio of 42.08dB, maximal structural similarity index measurement of 0.943, as well as maximum mutual information of 1.651

Novel techniques for registration of multimodal medical images

Medical image registration is a critical image processing task in many applications such as image-guided surgery (IGS) and image-guided radiotherapy. Herein, a novel automatic inter-modal affine registration technique is proposed based on the correlation ratio (CR) similarity metric firstly. The technique is demonstrated through registering intra-operative ultrasound (US) scans with magnetic resonance (MR) images of 22 patients from a publicly available database. By using landmark-based mean target registration errors (mTRE) for evaluation, the technique has achieved a result of 2.79$\pm$1.13 mm from an initial value of 5.40$\pm$4.31 mm. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a $p$-value of $0.0058$. To achieve this result, the MRI was deemed as the fix image ($I_f$) and the US as the moving image ($I_m$) and then $I_m$ was transformed to align with $I_f$. Covariance matrix adaptation evolutionary strategy (CMA-ES) was utilized to find the optimal affine transformation in registration of $I_m$ to $I_f$. In addition to quantitative validation using mTRE, the results were validated qualitatively by overlaying pre- and post-registration US and MRI to allow visual assessment of the alignment. The proposed fully automatic registration method significantly improved the alignment of MRI and US images and can therefore be used to aid neurosurgeons in resection of brain tumors. In addition to proposing new methods for registration of US and MRI, three different datasets of corresponding CT and US images of vertebrae were collected and presented. In the first dataset, two human patients’ lumbar vertebrae are presented and the US images are simulated from the CT images. The second dataset includes corresponding CT and US images of a phantom, made of post-mortem canine cervical and thoracic vertebrae. The third dataset includes the CT and US images of a lamb’s lumbar vertebrae. For the two latter datasets, 15 corresponding landmarks were provided and fiducial registration of the corresponding images was performed to acquire a silver standard ground truth of the registration. This dataset will be released online to allow validation of US-CT registration techniques

Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project