Speckle filtering techniques for different quality level of healthy kidney ultrasound images

The increasing reliance of modern medicine on diagnostic techniques such as computerized tomography, histopathology, magnetic resonance imaging, radiology and ultrasound imaging shows the importance of medical images [1]. Ultrasound (US) imaging is an imaging technique that is far the least expensive and most portable comparing to other standard medical imaging modalities. US imaging is a safe technique, easy to use, noninvasive nature and provides real time imaging, hence it is used extensively. But on the downside, ultrasound imaging has a poor resolution of image compared with other medical imaging instrument like Magnetic Resonance Imaging (MRI). US has wide spread application as a primary diagnostic aid of obstetrics and gynecology, due to the lack of ionizing radiation or strong magnetic fields. General US imaging applications include soft tissue organ and carotid arter

Assessment of Kidney Volume Measurement Techniques for Ultrasound Images

This study intends to assess and compare the accuracy of different methods for estimating the kidney volume of ultrasound images consist of volume measurement from length-based, area-based and surface-based. For length-based method, the ellipsoid formula was used and for surface-based method, the volume can be automatically obtained from 3D ultrasound system after some manual contouring. For area-based method, sets of ultrasound images with different number of slices were used. After manual contouring, the slices were reduced to 2D representation and the enclosed area between slices is calculated as the volume of the kidney. For a better assessment, experiment was also performed using egg phantom. As results, for egg phantom, by using water displacement method as gold standard volume, length-based method underestimates the volume for about 6% and surface-based method overestimate the volume for about 4%. For area-based method, the volume is also underestimated but varies with total number of slices used and can be as low as 2.6% and as much as 4.5%. By applying the same analysis to the kidney, it is thus concluded that for kidney volume measurement, area-based and surface based methods are more accurate but an automatic technique for contour detection need to be developed for repeatability usage of the methods. Length-based method on the other hand needs to have a new correctional factor implemented to ellipsoid formula for more accurate volume measurement

Feature Analysis of Kidney Ultrasound Image in Four Different Ultrasound using Gray Level Co-occurrence Matrix (GLCM) and Intensity Histogram (IH)

Misinterpretation analysis of ultrasound images has been huge issues in the world nowadays. Lack of skills and knowledge, as well as unclear ultrasound image due to the presence of speckle noise in ultrasound, are some factors lead to this issue. In this research, we compare 188 kidney ultrasound images from four different types of ultrasound machines, named as ultrasound A, B, C and D. Image pre-processing of images which involve cropping, enhancement, and filtering are performed before manual segmentation and texture analysis process to indicates the wanted region and improve contrast in each image. Texture analysis is performed using gray level co-occurrence matrix (GLCM) and intensity histogram (IH) to find differences and similarities in kidney image texture between all ultrasounds. Four GLCM parameters, contrast, correlation, energy and homogeneity and four parameters from IH (mean, standard deviation, variance, and skewness) used to indicate the most significant features between all ultrasound machines. Results show that contrast in GLCM is the most significant features that can be extracted from all four ultrasound machines and will be used in the classification process

Brain Signal Analysis Using Different Types of Music

Music is able to improve certain functions of human body physiologically and psychologically. Music also can improve attention, memory, and even mental math ability by listening to the music before performing any task. The purpose of this study is to study the relation between types of music and brainwaves signal that is differences in state of relaxation and attention states. The Electroencephalography (EEG) signal was recorded using PowerLab, Dual Bio Amp and computer to observes and records the subject brain activity in three condition; before, during, and after listening to different types of music; Light, Rock, Mozart, Al-Quran recitation and Jazz. The brainwave reaction of the subjects is compared during these three conditions. The hypothesis of this study is; Beta wave is increased at attention state and decrease at relaxation state. To analyze the data, Labchart7 software was used. The EEG data that has been recorded is processed using Digital Filter and the features of EEG signal is extracted and recorded by Data Pad that included in Labchart7 software. Then, the data is analyzed from mean of group features and also the average amplitude of Alpha and Beta wave. The results from this study are; the attention state of students is increased (Beta increased) while listening to Rock, AlQuran recitation and Mozart music when compared between during and before condition. Meanwhile, Beta wave is decreased while listening to Light and Jazz music thus showing the decrements of student’s state attention. Therefore, the result obtained may help students to choose better type of music or similar to help increases their concentration and focus while study

Histogram Equalization with Filtering Techniques for Enhancement of Low Quality Microscopic Blood Smear Images

This paper presents image enhancement and filtering techniques for microscope blood smear image, in order to improve low image quality that have characteristics: blurred, the diminished true color of objects which are cells , unclear boundary and low contrast between the cells and background. Therefore in this paper proposed histogram equalization (HE) technique followed with filtering techniques such as median filter. HE utilizing to adjust the contrast which based on intensity pixels values, hence able to measure image quality through image histogram as shown in results, while removing noise from the images using filtering and gamma correction parameter in order to distinguish between background and foreground (cells) to get clear borders also. These techniques have been implemented on 46 blood samples. The proposed method successfully improve the readability of the cells in the low quality of blood smear images this mean that contain more information with a good effectiveness which lead for the correct sickness detection and data analysis

Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix

This study proposes an approach of feature extraction of kidney ultrasound images based on five intensity histogram features and nineteen gray level co-occurrence matrix (GLCM) features. Kidney ultrasound images were divided into four different groups; normal (NR), bacterial infection (BI), cystic disease (CD) and kidney stones (KS). Before feature extraction, the images were initially preprocessed for preserving pixels of interest prior to feature extraction. Preprocessing techniques including region of interest cropping, contour detection, image rotation and background removal, have been applied. Test result shows that kurtosis, mean, skewness, cluster shades and cluster prominence dominates over other parameters. After normalization, KS group has highest value of kurtosis (1.000) and lowest value of cluster shades (0.238) and mean (0.649) while NR group has highest value of mean (1.000), skewness (1.000), cluster shades (1.000) and cluster prominence (1.000). CD group has the lowest value of skewness (0.625) and BI has the lowest value of kurtosis (0.542). This shows that these features can be used to classify kidney ultrasound images into different groups for creating database of kidney ultrasound images with different pathologies

Automatic non invasive kidney volume measurement based on ultrasound image

Variation in kidney sizes can be associated with different kidney diseases. Ultrasound is widely used in the measurement of kidney size in diagnostic process. However, the precision and accuracy of the result is low due to the manual measurement that is highly dependent on the skill and experience of the doctor. Hence, an automatic measurement system should be developed and implemented to measure the kidney size automatically from ultrasound image. One such method has been developed here in this study. First, samples of kidney ultrasound image were collected and analysed. Then, programming algorithm in which a combination of noise filtering method such as Gabor filter, Wiener filter, and sharpening methods were used to suppress the speckle noise on the ultrasound image while preserving the fine details. The kidney was then segmented from the image using Level set method in which the zero level set was evolved to minimize the overall energy function depending on the gradient flow. Comparison of pixel value was used to determine the maximum and minimum point which was utilized to find the length, width, and thickness. At last, volume of the kidney was calculated using ellipsoid formula. Few samples have been tested and the result showed that this system is viable and able to assist in the diagnosis of early stage kidney diseases

DFU Infection and Ischemia Classification: PSO-Optimized Deep Learning Networks

The recognition of infection and local perfusion (i.e., ischemic) status of diabetic foot ulcer (DFU) on a regular and timely basis is crucial to promote wound healing and prevent the development of unwanted complications. The conventional DFU assessment method is limited to scheduled clinic visits, impeding close monitoring of foot lesion progression and its chronicity. This paper presents an efficient Particle Swarm Optimization (PSO)-incorporated framework for classifying DFU infection and ischemia conditions using three deep learning models: AlexNet, GoogleNet, and EfficientNet-B0. The optimized system performed well in all evaluation metrics, ranging between 0.82 and 0.92 and near-perfect scores of 0.97 - 1, respectively, indicating the high performance and robustness of the system for the DFU infection and ischemia classification tasks. These results are better than the recent related studies using the same datasets. This system performs competitively with the deeper and heavier Efficient-B5 model, suggesting the efficiency of the proposed strategy without demanding an extensive network exploration process or elaborative feature selection process. The future of this work includes transferring the technology for DFU management using a mobile-based technology platform to improve outpatient care delivery through rapid recognition of DFU infection and their perfusion to optimize limb salvage outcomes

Unifying the seeds auto generation (SAGE) with knee cartilage segmentation framework: data from the osteoarthritis initiative

Purpose: Manual segmentation is sensitive to operator bias, while semiautomatic random walks segmentation offers an intuitive approach to understand the user knowledge at the expense of large amount of user input. In this paper, we propose a novel random walks seed auto-generation (SAGE) hybrid model that is robust to interobserver error and intensive user intervention. Methods: Knee image is first oversegmented to produce homogeneous superpixels. Then, a ranking model is developed to rank the superpixels according to their affinities to standard priors, wherein background superpixels would have lower ranking values. Finally, seed labels are generated on the background superpixel using Fuzzy C-Means method. Results: SAGE has achieved better interobserver DSCs of 0.94 ± 0.029 and 0.93 ± 0.035 in healthy and OA knee segmentation, respectively. Good segmentation performance has been reported in femoral (Healthy: 0.94 ± 0.036 and OA: 0.93 ± 0.034), tibial (Healthy: 0.91 ± 0.079 and OA: 0.88 ± 0.095) and patellar (Healthy: 0.88 ± 0.10 and OA: 0.84 ± 0.094) cartilage segmentation. Besides, SAGE has demonstrated greater mean readers’ time of 80 ± 19 s and 80 ± 27 s in healthy and OA knee segmentation, respectively. Conclusions: SAGE enhances the efficiency of segmentation process and attains satisfactory segmentation performance compared to manual and random walks segmentation. Future works should validate SAGE on progressive image data cohort using OA biomarkers