Medical Image Processing

This work evaluates deep learning-based myocardial infarction (MI) quantification using Segment cardiovascular magnetic resonance (CMR) software. Segment CMR software incorporates the expectation-maximization, weighted intensity, a priori information (EWA) algorithm used to generate the infarct scar volume, infarct scar percentage, and microvascular obstruction percentage. Here, Segment CMR software segmentation algorithm is updated with semantic segmentation with U-net to achieve and evaluate fully automated or deep learning-based MI quantification. The direct observation of graphs and the number of infarcted and contoured myocardium are two options used to estimate the relationship between deep learning-based MI quantification and medical exper-based results.

Early detection of lung cancer is the promising way of patient’s survival which can be recognize by using Computed Tomography (CT)
which is the technique in the detection of lung nodule compared to other imaging techniques. With the advancement of medical technology
Computer Aided Detection Schemes (CAD) are developed [2]. It provides higher accuracy and performance rate. Here the lung CT images
are taken as input, based on the support vector machine (SVM) it helps the doctors to perform image analysis. This paper focuses a study
concerning automatic detection of lung cancer nodules by different methods.

Image analysis is playing a very essential role in numerous research areas in the fields of science and technology, ranging from medical imaging to the computer science of automatic vision. Being involved in several applications, which are mainly based on a constant innovation of technologies, image analysis always requires different approaches and new algorithms, including continual upgrades and improvements of existing methods. Accordingly, the range of the analyses in which it is engaged can be considered as wide as the prospective future technologies. A challenge of image analysis is obtaining meaningful information by extracting specific features from images contained in large databases or during real-time acquisition. Among the problems requiring feature extraction are, to name a few, face detection and recognition, character recognition, and parametric determinations for augmented reality and other technologies. Another challenge is the secure encryption and decryption of multimedia data. These tasks demand highly sophisticated numerical and analytical methods. The contributions to this Special Issue provide a good overview of the most important demands and solutions concerning the abovementioned extraction, encryption, and decryption of data. In all the contributions, entropy plays a pivotal role. In the following, the reader can find subjects and problems according to the order of their publication.

Abstract—We investigate the use of signal envelope statistics to monitor and quantify structural changes during cell death using an in vitro cell model. Using a f/2.35 transducer (center frequency 20 MHz), ultrasound backscatter data were obtained from pellets of acute myeloid leukemia cells treated with a DNA-intercolating chemotherapy drug, as well as from pellets formed with mixtures of treated and untreated cells. Simulations of signals from pellets of mixtures of cells were generated as a summation of point scatterers. The signal envelope statistics were examined by fitting the Rayleigh and generalized gamma distributions. The fit parameters of the generalized gamma distribution showed sensitivity to structural changes in the cells. The scale parameter showed a 200% increase (p < 0.05) between untreated and cells treated for 24 h. The shape parameter showed a 50% increase (p < 0.05) over 24 h. Experimental results showed reasonable agreement with simulations. The results indicate that high-frequency ultrasound signal statistics can be used to monitor structural changes within a very low percentage of treated cells in a population, raising the possibility of using this technique in vivo. (E-mail: mkolios@ryerson.ca) © 2005 World Federation for Ultrasound in Medicine & Biology.

Key Words: Tissue characterization, High-frequency ultrasound, Envelope statistics, Generalized gamma, Rayleigh, Treatment monitoring, Cell/nucleus structure, Ultrasonics, Acute myeloid leukemia, Computer simulation.

Skin diseases are among the most common health problems worldwide. In this article we proposed a method that uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the colour skin images to extract significant features and later identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

—Hough transform (HT) is a typical method to detect or segment geometry objects from images. In this paper, we study the principle of Hough Transform and its mathematical expressions, and try to use a new approach based on Hough transform for quick line and circle detection in image processing. Our method accurately detected some simple graphics, such as straight line of different direction, circles of different detection, thickness and different number. The results show that our method is less memory consumption and calculated fast, which could be applied for line detection and segmentation in 3D ultrasonic image.

Medical image is the essential tool for patient diagnosis and treatment. One of it is important properties it large considerable size, so retrieving large size of medical image to another hospital and clinic for consultant and diagnosis raise issue the main one is the long time that it takes to retrieve large medical image file. Our main objective is review how the technique presented using cloud deal with balancing the parallel retrieval of the medical image.

Aim-To determine the gender using mental foramen as landmark on a panoramic radiographs in selected North Gujarat population. Objective-1. To evaluate and compare the superior border of mental foramen to lower border of mandible(S-L) and inferior border of mental foramen to lower border of mandible(I-L) value in males and females bilaterally. 2. To compare the S-L and I-L between right and left side in males and female 3. To utilize above measurements for gender determination. Materials and Method-Sixty panoramic radiographs were selected for the analysis of mental foramen. Tangents were drawn through the superior and inferior borders of the foramen (S-L and I-L respectively) and perpendicular from the tangents to the lower border of the mandible bilaterally. Digital verniar caliper was used for the distance measurement from S-L and I-L. The data obtained was tabulated and subjected to statistical analysis. Result-The analyzed data of study showed that the mean values of comparison of S-L as well as I-L in males and females were significantly higher in males as compared to females. The comparison of SL and IL on right and left side in the same patient was without any significant difference. Conclusion-The results of present study concluded a definite sexual dimorphism in the position of the mental foramen from the base of the mandible; this method can applied in mass disaster where the fragments of mandible are available.

Background: The current medicine has as main goal to cure the disease, but this requires that the stages ranging from early examinations and diagnosis to end of treatment are done as quickly as possible in order to prevent the spread of the disease and its irreversible effects, this scenario is of great help the use of technologies that reduce the examination time analysis and enable speed diagnosis such as artificial intelligence.
Objective: Conduct a survey of scientific literature on application of artificial intelligence in the development of medical diagnostics with emphasis on neuro-fuzzy networks. Method: scientific databases were consulted of Science Direct and EBSCO using the descriptor Neural Fuzzy Diagnosis to perform the review was considered the period of 2010 to the first half of 2015.
Results: 360 publications were located, after applying the criteria inclusion, 14 were selected for the study, it was observed that 78.6% of the publications was conducted by EBSCO. 28.6% of the publications are applications in diagnosis of Cancer and 28.6% are applications in diagnosis of heart disease, 14.3% are applications in diagnosis of brain diseases, the other 35.7% are related to eye diseases, infectious contagious, cardiovascular, diabetes and jaundice. Specifically, 21.4% of the studies are in arrhythmia and 14.3% in Breast Cancer and 14.3% for depression. Journals that published most are Neural Computing and Applications and Expert Systems with 28.6% and 14.3% of publications respectively. It was observed that the number of publications on the subject has doubled from 2013, from 2 to 4 publications a year with the same result in 2014.
Conclusions: Through the survey we found that there is little published about diseases like tuberculosis, diabetes, leukemia, Glaucoma, Ovarian Cancer and Hypertension, so it is necessary further investigation, deepening of knowledge and publishing studies investigating sharply each of these aspects. Only from solid knowledge can develop and deploy adequate support systems to the development of medical diagnostics.

Segmentation of brain tumor from medical images is an interesting topic which is investigated by many researchers. It is important to locate tumor at an early stage so that it can be easily healed and can be used for further diagnosis. There are different imaging techniques which are used in segmentation of brain tumor. Among them, Magnetic Resonance Imaging (MRI) is most widely used radiological tool as it is radiation free in nature. For detecting the size, shape, and location of the tumor many segmentation algorithms were used. In this paper, an exhaustive study of brain tumor from MRI images has made by using different techniques. A comparative study and performance evaluation of different techniques based on certain performance metrics are also discussed in this paper.

Background and objective: Accurate, reliable, efficient, and precise measurements of the lumen geom- etry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter- adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements. Methods: The automated algorithms proposed for the segmentation of carotid lumen are broadly classi- fied into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmen- tation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies. Results: Even though, we have found more boundary-based approaches compared to region-based ap- proaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients. Conclusions: We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial di- ameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information.

An electrogoniometer is a device used to measure the angle of joints during dynamic movements. Flexion ,extension , abduction and adduction are the motions that are measured by an electrogoniameter .
This study has both software and hardware parts. In its hardware part, these elements have been used: A PIC16F877 as the microcontroller, two 32 kilobytes memories, an 8 channel A/D IC, and elements like resistors, capacitors and some other elements. The Lab VIEW software has been used for receiving data. This software shows the gathered information thorough printer port which is sent from the external memories. But anyway the goal of using such different tools is designing and synthesis of wired pants that records the angels of feet and legs (8 JOINTS) simultaneously and showing them in the computer. The most advantage of these pants is its dynamicmovement measuring.

Skin diseases are among the most common health
problems worldwide. In this article we proposed a method that
uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed
forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the
colour skin images to extract significant features and later
identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

—The precise and accurate diagnose of an ailment is foundation of an effective medical aid. For this the medical fraternity relies heavily upon the various radiographic scans which help in the diagnosis, staging and treatment of a particular disease. These imaging modalities depict the anatomical, functional as well as molecular level information about body organs. In this paper we have presented the basic principle, advancement and applications of some of the major imaging modalities. These multi-sensor images depicting complimentary information are fused together to generate a composite image with higher amount of information.

Non-invasive heart rate measurement is essential in medical and sport sciences. Typically, the first stage of heart rate measurements is obtained from non-invasive sensors. In this paper, the data from video sequences of a subject cycling are processed using short-time Fourier transform (STFT) to indicate the heart rate of the subject. The choice of using STFT is its ability to provide more accurately localized temporal and frequency information, especially for the rapidly changing heart rate pattern during the exercise routine. Experimental results show the proposed method can provide an acceptable result and the root mean square error is less than 2.5 beats per minute (BPM) for heart rate variations between 80 BPM and 130 BPM. © 2013 IEEE.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6704640&tag=1

In this paper, we have summarized the necessary statistical theory behind an image reconstruction, and it discusses the idea of super resolution as an inverse problem. Moreover, it presents the various contributions of various techniques to improve spatial resolution of images, video, and magnetic resonance images using super resolution methods. Subsequently, it portrays the major findings pertaining to the study. Additionally, we give a glimpse of the numerous factors influencing the performance of super resolution.

Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern manufacturing atmosphere. The selection decisions become more multifaceted due to continuous incorporation of advanced features and facilities as the decision makers in the manufacturing environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available. The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a novel model in which all performance ratings are converted into numerical values greater than and equal to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the index of corresponding normalized ratings those are multiplied to obtain the resultant ...

Diabetes is a serious health issue faced by the society. Classification of tissues in a diabetic wound is highly essential to monitor the healing progress of the wound. The standard manual tissue classification methods utilized by the clinicians are prone to inaccuracy and is highly dependant on the knowledge and experience of the expert. Moreover, since the wounds may form at a body part that is difficult to access, assessing of the wound becomes time-consuming and causes distress to the patient. Hence, an automatic diabetic wound classification system is required for accurate assessment of the wound. In this paper, we offer tissue classification system for diabetic ulcers that utilizes Fuzzy C-Means and colour analysis using colour features to cluster the digital images. The clusters are initially labelled by calculating the minimum Euclidean Distance of the cluster center from the reference points. Each cluster was further analyzed by using spatial relation to label the smaller blobs, and tissue relation to label larger blobs. Granulation and Epithelial were further evaluated by computing the shiny regions caused by the reflection of light on wetter regions of the wound. The proposed algorithm will segment wound tissues into granulation, slough, eschar, and epithelial tissues and display the segmented regions along with the percentage area of each type of tissue. This system can be used by the diagnostician to effectively and easily monitor the healing progress of the wound.

Background: The second chief foundation of enduring visual deficiency around the world is Glaucoma. Timely exposure of this disease can confine the advancement in disease progression. Calculating Cup to Disk Ratio is amongst the effective ways for figuring out the presence of glaucoma. Method and Materials: In this paper, a Cup to Disk Ratio and Rim to Disk Ratio calculation based glaucoma detection method is proposed. The proposed technique comprises of three main phases. In the preliminary stage, the image is preprocessed and optic disk is segmented out using Fourier Transform, Active Contour and Circular Hough Transform followed by ocular cup segmentation stage. Ocular cup segmentation is carried out through morphological operations, Circular Hough Transform, Median Filter and Active Contour Segmentation algorithms. The outcomes of proposed technique are tested on 200 images taken from four different databases including comparison with the results of approaches proposed and implemented in the prospect of ocular cup and optic papilla based segmentation. Results: The results achieved through the proposed technique showed that the proposed strategy acquired high exactness in the segmentation of optic papilla and ocular cup hence obtained RDR and CDR values near the clinical CDR values.

Alzheimer’s Disease is a severe neurological brain disorder. It destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. Alzheimer’s Disease is not curable, but earlier detection can help improve symptoms in a great deal. Machine learning techniques can vastly improve the process for accurate diagnosis of Alzheimer’s Disease. In recent days deep learning techniques have achieved major success in medical image analysis. But relatively little investigation has been done to applying deep learning techniques for Alzheimer’s Disease detection and classification. This paper presents a novel deep learning model for multi-Class Alzheimer’s Disease detection and classification using Brain MRI Data. We design a very deep convolutional network and demonstrate the performance on the Open Access Series of Imaging Studies (OASIS) database.

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and
many algorithms and filters have been planned to improve the image information. There are various noise removal
procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the
overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising
algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).
OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF
methods which are effective, efficient for denoising various kinds of images.
To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of
various degrees of noise in the image.
To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its
degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc.
METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising
algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels
in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt &
pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF,
UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained
for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved
from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for
lower to higher image noise densities levels.

Breast cancer is defined as a malignant neoplasm disease originating from the parenchyma. This disease is most commonly afflicted by women and is the second killer after lung cancer. By the end of 2012, the WHO shows the prevalence of breast cancer worldwide reaching 6.3 million spread across 140 countries. Symptoms of the disease are diagnosed using mammogram. The results will be examined by paramedics, to find out if the cancer is malignant or not. However, the accuracy of the diagnoses obtained is directly proportional to the level of paramedical expertise. The general objective of this research aims to classify breast cancer types based on the extracted breast cancer features, and the specific purpose is to offer a hybrid machine learning method. To help increase the accuracy of the diagnosis, through this
study, a CAD-based scheme was developed by applying a combination of Wrapper and Naive Bayes algorithms. The Wrapper algorithm is used at the feature selection stage, while the Naive Bayes algorithm is used at the classification stage. There are 683 data taken from the UCI Knowledge Repository consisting of two class, namely 444 benign cancers and 239 malignant cancers, with 9 types of attributes. The data is divided into two groups, with the various amount for each class in each group. The first group was used as a training group for the feature selection stage, and the second group as the testing group for the classification stage. The classification results show the accuracy up to 99.27%, sensitivity and specificity on each up to 99.30%. Based on these results, the proposed scheme is expected to contribute in the development of CAD for the diagnosis of breast cancer.

Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater amount of morbidity and mortality since most patients often have asymptomatic abdominal condition and symptoms, which is often recognized late hence, the abdomen has been the third most common cause of damage to the human body. That notwithstanding, there may be improved outcome when the condition of an abdominal organ is detected earlier. Over the years supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods performs well when the training data used is a representative of the data targeted but, the methods require large manual annotated data and has adaptation problem. The semi-supervised methods are fast but records poor performance than the supervised if assumptions made about the data fails to hold. Current state-of-the art methods of supervised segmentation are largely based on deep learning techniques due to its good accuracy and success in real world applications though it requires large amount of training data for automatic feature extraction else deep learning can hardly be used. As regards the semi-supervised methods of segmentation self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrives on its convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches connection and gaps are identified, and prospective future research opportunities are enumerated.

یکی از مهمترین مسائل و دغدغه های مطرح در سیستم های قدرت ، مسئله قطع و وصل مطلوب جریان الکتریسیته در رده های ولتاژی بالاتر از 6 کیلو ولت می باشد. تجهیزات بسیاری تاکنون در این زمینه طراحی و پیاده سازی شده اند که  توانسته اند تا حد زیادی مشکلات پیش آمده را برطرف نمایند. از میان این تجهیزات می توان به کلیدهای قدرت روغنی ، کلیدهای هوایی، کلیدهای خلاء و کلیدهای اس اف 6 اشاره کرد. در عمل دیده شده که کلیدهای  اس اف 6 بسیار کاربردی تر و موثر تر از بقیه موارد به خصوص در نیروگاه های اتمی عمل کرده اند.

Telecommunication plays an important role in our day to day life. It has practically revolutionized our life especially in the field of long distance communication. Despite all these advancements visually impaired people have no/limited access to these technologies.In order to bridge the gap between blind people and technological advancement, we have come up with a novel idea of designing a SMS reading system using vibration motors .Here the user sends the text messages to blind person's mobile number which is connected to pic microcontroller which reads SMS using GSM module and converts the English letters of SMS into Braille language. This system is provided with vibration motors that vibrate on a definite pattern, provided that the visually impaired person has some knowledge about Braille language. With the help of six relays the pic microcontroller vibrates the vibration motors which are pasted on a glove and the vibration can be felt accordingly. This proposed system is very effective for receiving text messages and can be modified for sending text messages also.

The disorders identified with retina of the eye such as: Diabetic Retinopathy (DR), age-related Macular Degeneration (AMD), Glaucoma, etc. can cause visual impairments. The retinal fundus pictures of the patients can be acquired with a computerized fundus camera. It can then be used as machine learning application against the manual technique for the detection and prevention of DR. In this research, the retinal fundus image obtained from Himalaya Eye Hospital Pokhara, MESSIDOR database and EyePACS are used for the detection of DR along with its severity label. In the preprocessing phase, the images are first converted to grayscale images. Histogram equalization technique is performed to adjust image intensity in order to enhance contrast. Median filtering, a non-linear digital filtering technique, is implemented to remove noise present in the image. It too helps to improve the result for further processing. Blood vessels are detected and optic discs are identified and removed. Exuda...

A Knight Tour problem is an ancient puzzle which remains as a focus of current researcher. The objective of a Knight Tour algorithm is to find out a solution with construct a closed moving path by a knight for visiting each square of a chessboard by only once. This paper proposes a new structural Knight Tour algorithm by four knights moving all together with heuristic preset rules. This new proposed method fulfills any board size of 4n+2 where n&gt;1 with lower execution time and without the needs of any brute force method and backtracking method.