Current Medical Imaging - Volume 15, Issue 8, 2019

The huge information of healthcare data is collected from the healthcare industry which is not “mined” unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

Background: One of the primary causes of sleep disorders, depth of anesthesia, coma, encephalopathy’s and brain death in the world is epilepsy. EEG is most often used to diagnose epilepsy which causes the abnormalities in EEG readings. Different high-resolution anatomical imaging techniques are used to detect these abnormalities like MRI, PET, CT, etc. Methods: SWT method will be an enhanced system from wavelet transform. It may be fit for the signal for time-invariant on the break down also enhance those force of indicator denoising. SWT additionally employs upsampled technique at every level of decay for those signs. The decay of SWT produces the coefficients from claiming close estimation and points in the same length. The DWT will be actualized by a channel bank that decomposes those indicators over progressive coarser approximations. The output of the low pass and high pass filter coefficients is decomposed to the next level and further proceeds up to N levels. The yield of the wavelet decay may be that close estimation and the point of interest coefficients which would get to each level of decay. This system consists of five main processing steps: acquisition, pre-processing, feature extraction, feature selection and classification. Results: This paper overviews some of the current state-of-the-art IOT systems and presents the statistical-based algorithm used for each processing step. Conclusion: This paper also provides a comparison of the performance of the existing approaches.

Objective: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI). Methods: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction. Results: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64). Conclusion: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.

Background: The proposed method uses random adjustments in the online video quality based on the bandwidth allocated over Dynamic Adaptive Streaming over HTTP (DASH) streaming service. Aim: The main objective is to improve the video quality from DASH-HTTP servers with variable bandwidth. Here, the system is adjusted dynamically for providing best video quality services based on the requirement of the user. Methods: In order to achieve such objective, the DASH service is assigned with three modules. Initially, the quality is adjusted dynamically using Fractional Brownian Motion and Random Waypoint Mobility (FBM-RWP) model. This initial model schedules the packets in sub-streams based on the priority as per the requirement of the user. The final model uses the Proportional Integral Derivative (PID) quality control algorithm for the past and future prediction of quality based on bandwidth allocation. This feedback of quality is used by the FBM-RWP model to prioritize the packets in the sub-streams. The entire process works by matching the bit rate of video streaming with the user required quality. Resuslts: The technique concentrates mostly on medical videos for improving the live video streaming in case of medical emergencies. The performance of the proposed method is compared with the conventional DASH services. The results proved that the proposed method performs better in terms of reduced error and improved throughput.

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Results: The efficiency of the system is implemented in tomato, grape, cucumber plants leaf images and the results are analyzed in terms of the error rate, sensitivity, specificity, accuracy and time. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

Background: To evaluate the hysterosalpingography (HSG) findings of women with infertility in a tertiary center located in central Turkey. Methods: A total of 1,996 patients undergoing the HSG procedure for the investigation of infertility from April 2012 to 2017 were retrospectively evaluated using the archives of the reproductive endocrinology and radiology departments. Demographic and clinical characteristics of patients with normal HSG findings (n = 1,549) and patients with abnormal HSG findings (n = 447) were compared, and the distribution of pathologies on the HSG examinations was evaluated as well. Results: There were statistically significant differences between patients with normal and abnormal HSG findings in terms of age (25.68 ± 4.54 vs. 35.87 ± 2.65, p < 0.001), type (for secondary) and duration of infertility [43.1% vs. 50.6% (p = 0.006); 7 (1-22) vs. 2 (1-12) (p < 0.001), respectively], and baseline follicle stimulating hormone and estradiol levels [7.22 ± 1.38 vs. 7.55 ± 1.42 (p < 0.001); 45.54 ± 9.92 vs. 44.40 ± 9.99 (p < 0.001), respectively]. Among a total of 1,996 HSG examinations, 447 (22.39%) showed abnormalities, of which 237 (11.87%) were associated with tubal pathologies, 163 (8.17%) with uterine pathologies, and 47 (2.35%) with a combination of both. While the most common tubal pathology was one-sided distal tubal occlusion (2.91%), the most common uterine pathology was filling defects (4.16%). Conclusion: HSG is the most commonly used, well-tolerated, low-cost, and safe radiological procedure to use for the investigation of the causes of female infertility.

Background: Challenges in visual identification of laryngeal disorders lead researchers to investigate new opportunities to help clinical examination. This paper presents an efficient and simple method which extracts and assesses blood vessels on vocal fold tissue in order to serve medical diagnosis. Methods: The proposed vessel segmentation approach has been designed in order to overcome difficulties raised by design specifications of videolaryngostroboscopy and anatomic structure of vocal fold vasculature. The limited number of medical studies on vocal fold vasculature point out that the direction of blood vessels and amount of vasculature are discriminative features for vocal fold disorders. Therefore, we extracted the features of vessels on the basis of these studies. We represent vessels as vascular vectors and suggest a vector field based measurement that quantifies the orientation pattern of blood vessels towards vocal fold pathologies. Results: In order to demonstrate the relationship between vessel structure and vocal fold disorders, we performed classification of vocal fold disorders by using only vessel features. A binary tree of Support Vector Machine (SVM) has been exploited for classification. Average recall of proposed vessel extraction method was calculated as 0.82 while healthy, sulcus vocalis, laryngitis classification accuracy of 0.75 was achieved. Conclusion: Obtained success rates showed the efficiency of vocal fold vessels in serving as an indicator of laryngeal diseases.

Background: Mounier Kuhn Syndrome (MKS) is a rare congenital anomaly characterized by abnormal dilatation of the trachea and main bronchi. The aim of this study is to discuss tracheal volume measurement in MKS, and the pathologies accompanying MKS, especially pulmonary artery enlargement. Materials and Methods: 38 patients, 18 of whom were diagnosed with MKS and 20 as control group, were included in the study. Trachea volume and pulmonary artery diameter were measured through thorax-computed tomography (CT) images of the patients. Accompanying pathologies were recorded. Results: In the measurements done through the CT scans, the trachea volume was found to be 25.45 cm3 in the control group and 44.17 cm3 in the patient group. The most frequent accompanying pathologies were tracheal diverticulum, bronchiectasis and pulmonary artery enlargement. Conclusion: In patients with MKS, there is a significant difference in volume calculation as in trachea diameter. Though bronchiectasis and tracheal diverticulum are known as pathologies most frequently accompanying MKS, to the knowledge of the researchers, pulmonary artery enlargement due to the increase in pulmonary truncus diameter was first emphasized in this article.

Background: Privacy protection has been a critical issue in the delivery of medical images for telemedicine, e-health care and other remote medical systems. Objectives: The aim of this proposed work is to implement a secure, reversible, digital watermarking technique for the transmission of medical data remotely in health care systems. Methods: In this research work, we employed a novel optimized digital watermarking scheme using discrete wavelet transform and singular value decomposition using cuckoo search algorithm based on Lévy flight for embedding watermark into the grayscale medical images of the patient. The performance of our proposed algorithm is evaluated on four different 256 x 256 grayscale host medical images and a 32 x 32 binary logo image. Results: The performance of the proposed scheme in terms of peak signal to noise ratio was remarkably high, with an average of 55.022dB compared to other methods. Conclusion: Experimental results reveal that the proposed method is capable of achieving superior performance compared to some of the state-of-art schemes in terms of robustness, security and high embedding capacity which is required in the field of telemedicine and e-health care system.

Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem. Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis. Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process. Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine. Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.