scholarly journals An Intelligent cluster-based segmentation using Wavelet Features:Application to Medical Images

2010 ◽  
Author(s):  
Thavavel V ◽  
JafferBasha J
Keyword(s):  
Medical Images ◽  
Automatic Segmentation ◽  
A Priori ◽  
User Interaction ◽  
Region Of Interest ◽  
Feature Space ◽  
Discrete Wavelet ◽  
Wavelet Frames ◽  
Translation Invariant ◽  
Spherical Coordinate

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of ‘a priori’ knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.

scholarly journals An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 1063-1078
Author(s):  
P. Sreenivasulu ◽  
S. Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Huffman Encoding ◽  
Encoding Method

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

scholarly journals StoolNet for Color Classification of Stool Medical Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1464 ◽  
Author(s):  
Ziyuan Yang ◽  
Lu Leng ◽  
Byung-Gyu Kim
Keyword(s):  
Medical Images ◽  
Low Cost ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Training Sample ◽  
Color Classification ◽  
Processing Technologies ◽  
Heavy Burden ◽  
The Impact

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

scholarly journals Fproi-GAN with Fused Regional Features for the Synthesis of High-Quality Paired Medical Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiale Dong ◽  
Caiwei Liu ◽  
Panpan Man ◽  
Guohua Zhao ◽  
Yaping Wu ◽  
...  
Keyword(s):  
Prediction Models ◽  
Medical Images ◽  
A Priori ◽  
Region Of Interest ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Regional Feature ◽  
Tissue Contrast ◽  
Structural Consistency

The use of medical image synthesis with generative adversarial networks (GAN) is effective for expanding medical samples. The structural consistency between the synthesized and actual image is a key indicator of the quality of the synthesized image, and the region of interest (ROI) of the synthesized image is related to its usability, and these parameters are the two key issues in image synthesis. In this paper, the fusion-ROI patch GAN (Fproi-GAN) model was constructed by incorporating a priori regional feature based on the two-stage cycle consistency mechanism of cycleGAN. This model has improved the tissue contrast of ROI and achieved the pairwise synthesis of high-quality medical images and their corresponding ROIs. The quantitative evaluation results in two publicly available datasets, INbreast and BRATS 2017, show that the synthesized ROI images have a DICE coefficient of 0.981 ± 0.11 and a Hausdorff distance of 4.21 ± 2.84 relative to the original images. The classification experimental results show that the synthesized images can effectively assist in the training of machine learning models, improve the generalization performance of prediction models, and improve the classification accuracy by 4% and sensitivity by 5.3% compared with the cycleGAN method. Hence, the paired medical images synthesized using Fproi-GAN have high quality and structural consistency with real medical images.

ROI-BASED 3D HUMAN BRAIN MAGNETIC RESONANCE IMAGES COMPRESSION USING ADAPTIVE MESH DESIGN AND REGION-BASED DISCRETE WAVELET TRANSFORM

2010 ◽  
Vol 08 (03) ◽  
pp. 407-430 ◽  
Author(s):  
EMAD FATEMIZADEH ◽  
PARISA SHOOSHTARI
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Compression Ratio ◽  
Medical Images ◽  
Region Of Interest ◽  
Adaptive Mesh ◽  
Magnetic Resonance Images ◽  
Discrete Wavelet ◽  
Object Based ◽  
The Difference

Due to the large volume required for medical images for transmission and archiving purposes, the compression of medical images is known as one of the main concepts of medical image processing. Lossless compression methods have the drawback of a low compression ratio. In contrast, lossy methods have a higher compression ratio and suffer from lower quality of the reconstructed images in the receiver. Recently, some selective compression methods have been proposed in which the main image is divided into two separate regions: Region of Interest (ROI), which should be compressed in a lossless manner, and Region of Background (ROB), which is compressed in a lossy manner with a lower quality. In this research, we introduce a new selective compression method to compress 3D brain MR images. To this aim, we design an adaptive mesh on the first slice and estimate the gray levels of the next slices by computing the mesh element's deformations. After computing the residual image, which is the difference between the main image and the estimated one, we transform it to the wavelet domain using a region-based discrete wavelet transform (RBDWT). Finally, the wavelet coefficients are coded by an object-based SPIHT coder.

scholarly journals Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Motor Cortex ◽  
Muscle Contraction ◽  
Muscle Relaxation ◽  
Time Windows ◽  
Automatic Segmentation ◽  
Discrete Wavelet ◽  
Long Duration ◽  
Binary Segmentation ◽  
Flexor Digitorum ◽  
Muscle Connectivity

Effective segmentation of electromyography (EMG) burst that synchronizes with electroencephalography (EEG) for long-duration recording is important steps to better understand the quantification of brain-muscle connectivity in periodic motoric activities. The work proposes an alternative automatic EMG segmentation scheme consists of four main steps, i.e. denoising of EMG burst signal using discrete wavelet transform, enveloping signal using time-windows averaging of RMS amplitude, an adaptive threshold to detect start/end burst envelope with accommodation of muscle contraction characteristic and the final step is conversion enveloping signal to binary segmentation signal.The proposed scheme is evaluated to detect contraction period/duration of EMG for the subject under repetitive holding and releasing grasp using a physiotherapy device. During exercise, the bio-amplifier board is customized to acquire simultaneous EEG and EMG from the region of flexor digitorum superficialis (FDS) of muscle and cortical motor of the brain, with total 284 EMG burst that counting by manual segmentation. The automatic segmentation can detect the total EMG burst by 6.25% error of false burst detection.The usefulness of proposed scheme is also tested to association analysis according to the power of EMG burst and the power of mu-wave of EEG recorded on the motor cortex. The changing trend of the power of mu-wave associated with muscle relaxation, muscle contraction strength and the synchronization level on the motor cortex during exercise are analyzed with integrated information that is relevant with biofeedback concept. The results demonstrate that proposed scheme has potential to be an effective method for the evaluation of biofeedback rehabilitation exercise.

scholarly journals Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

2020 ◽  
Vol 2 (Supplement_4) ◽  
pp. iv3-iv14
Author(s):  
Niha Beig ◽  
Kaustav Bera ◽  
Pallavi Tiwari
Keyword(s):  
Treatment Response ◽  
A Priori ◽  
Point Mutations ◽  
Region Of Interest ◽  
Next Generation Sequencing Data ◽  
Tumor Segmentation ◽  
Sequencing Data ◽  
Statistical Correlations ◽  
Data Driven Approach ◽  
Mri Scans

Abstract Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

Signal quantitative analysis using customizable perfect-translation-invariant complex wavelet functions

2014 ◽  
Vol 12 (04) ◽  
pp. 1460010 ◽  
Author(s):  
Hiroshi Toda ◽  
Zhong Zhang
Keyword(s):  
Quantitative Analysis ◽  
Frequency Band ◽  
Wavelet Transforms ◽  
Wavelet Coefficient ◽  
Discrete Wavelet ◽  
Digital Signals ◽  
Orthonormal Wavelet ◽  
Translation Invariant ◽  
Fast Calculation ◽  
Complex Wavelet

In this paper, we introduce several methods of signal quantitative analysis using the perfect-translation-invariant complex wavelet functions (PTI complex wavelet functions), which are used in our proposed perfect-translation-invariant complex discrete wavelet transforms (PTI CDWTs) and can be designed by customization. First, using PTI complex wavelet functions, we define the continuous wavelet coefficient (CWC). Next, using orthonormal wavelet functions in the classical Hardy space, we analyze the CWC, and show that, using a CWC, we can measure the energy of a customizable frequency band, and additionally, using numbers of CWCs, we can measure the energy of the whole frequency band. Next, we introduce the fast calculation method of CWCs and show the applicability of the PTI CDWTs to digital signals. Based on them, we introduce some examples of signal quantitative analysis, including the methods to obtain instantaneous amplitude, instantaneous phase and instantaneous frequency. Additionally, we introduce the energy measurement of the whole frequency band using the PTI DT-CDWT, which is one of our proposed PTI CDWTs.

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