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   2015| January-March  | Volume 5 | Issue 1  
    Online since September 18, 2019

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Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier
Morteza Moradi Amin, Saeed Kermani, Ardeshir Talebi
January-March 2015, 5(1):49-58
DOI:10.4103/2228-7477.150428  PMID:25709941
Acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three L1, L2, and L3 and could be detected through screening of blood and bone marrow smears by pathologists. Due to being time-consuming and tediousness of the procedure, a computer-based system is acquired for convenient detection of Acute lymphoblastic leukemia. Microscopic images are acquired from blood and bone marrow smears of patients with Acute lymphoblastic leukemia and normal cases. After applying image preprocessing, cells nuclei are segmented by k-means algorithm. Then geometric and statistical features are extracted from nuclei and finally these cells are classified to cancerous and noncancerous cells by means of support vector machine classifier with 10-fold cross validation. These cells are also classified into their sub-types by multi-Support vector machine classifier. Classifier is evaluated by these parameters: Sensitivity, specificity, and accuracy which values for cancerous and noncancerous cells 98%, 95%, and 97%, respectively. These parameters are also used for evaluation of cell sub-types which values in mean 84.3%, 97.3%, and 95.6%, respectively. The results show that proposed algorithm could achieve an acceptable performance for the diagnosis of Acute lymphoblastic leukemia and its sub-types and can be used as an assistant diagnostic tool for pathologists.
  524 59 29
A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement
Seyed Hossein Rasta, Mahsa Eisazadeh Partovi, Hadi Seyedarabi, Alireza Javadzadeh
January-March 2015, 5(1):40-48
DOI:10.4103/2228-7477.150414  PMID:25709940
To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest coefficients of variation in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low coefficients of variation . The contrast limited adaptive histogram equalization contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as contrast limited adaptive histogram equalization, for fundus images presented good potentials in enhancing the vasculature segmentation.
  486 65 16
An improved method for liver diseases detection by ultrasound image analysis
Mehri Owjimehr, Habibollah Danyali, Mohammad Sadegh Helfroush
January-March 2015, 5(1):21-29
DOI:10.4103/2228-7477.150387  PMID:25709938
Ultrasound imaging is a popular and noninvasive tool frequently used in the diagnoses of liver diseases. A system to characterize normal, fatty and heterogeneous liver, using textural analysis of liver Ultrasound images, images, is proposed in this paper. The proposed approach is able to select the optimum regions of interest of the liver images. These optimum regions of interests are analyzed by two level wavelet packet transform to extract some statistical features, namely, median, standard deviation, and interquartile range. Discrimination between heterogeneous, fatty and normal livers is performed in a hierarchical approach in the classification stage. This stage, first, classifies focal and diffused livers and then distinguishes between fatty and normal ones. Support vector machine Support vector machine and k-nearest neighbor classifiers have been used to classify the images into three groups, and their performance is compared. The Support vector machine classifier outperformed the compared classifier, attaining an overall accuracy of 97.9%, with a sensitivity of 100%, 100% and 95.1% for the heterogeneous, fatty and normal class, respectively. The Acc obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists and experts in liver diseases interpretation.
  434 22 7
Multi-scale morphological image enhancement of chest radiographs by a hybrid scheme
Fatemeh Shahsavari Alavijeh, Homayoun Mahdavi-Nasab
January-March 2015, 5(1):59-68
DOI:10.4103/2228-7477.150435  PMID:25709942
Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization , Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.
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Chaotic particle swarm optimization with mutation for classification
Zahra Assarzadeh, Ahmad Reza Naghsh-Nilchi
January-March 2015, 5(1):12-20
DOI:10.4103/2228-7477.150380  PMID:25709937
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization , it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.
  381 30 8
Electrocardiogram based identification using a new effective intelligent selection of fused features
Hamidreza Abbaspour, Seyyed Mohammad Razavi, Nasser Mehrshad
January-March 2015, 5(1):30-39
DOI:10.4103/2228-7477.150389  PMID:25709939
Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.
  321 16 1
Epileptic seizure prediction based on ratio and differential linear univariate features
Jalil Rasekhi, Mohammad Reza Karami Mollaei, Mojtaba Bandarabadi, César A Teixeira, António Dourado
January-March 2015, 5(1):1-11
DOI:10.4103/2228-7477.150371  PMID:25709936
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance.
  309 20 7
99m Tc-phytate lymphoscintigraphy for detection of sentinel node: Preliminary results of the first year's clinical experience in Isfahan, Iran
Masoud Moslehi, Ahmad Shanei, Seyyed Mohammad Reza Hakimian, Golshan Mahmoudi, Milad Baradaran-Ghahfarokhi
January-March 2015, 5(1):69-74
DOI:10.4103/2228-7477.150440  PMID:25709943
Sentinel lymph node is the first regional lymph node that drains the lymph from the primary tumor. It is potentially the first node to receive the seeding of lymph-borne metastatic cells. This study aimed to discuss lymphoscintigraphy procedural guidelines for detection of sentinel node using 99mTc-Phytate in Isfahan, Iran. Moreover, the preliminary results of the first year's clinical experience of lymphoscintigraphy in Isfahan, Iran are also presented. A total of 36 consecutive sentinel node procedures were performed following our protocol in March 2013 to March 2014. For all 36 patients, after intradermal injection of 0.5-1 mCi of 99mTc-Phytate, 5, 30 and 120 min with hands up lymphoscintigraphy was performed. All procedures were performed in a 1-day setting with 99mTc-Phytate injection in intradermal volume of about 0.1 cc. At 5, 30 and 120 min after injection, anterior and lateral images (4 min), were acquired using gamma-camera (energy 140 keV, window 15-20% and LEHR collimator). For all patients, at least one axillary sentinel lymph node was detected. For three patients, 2 SNs were seen. The images 5 min after injection showed at least one axillary sentinel node in 18 of 36 patients. However for the remaining patients, more delayed images (after 30 and 120 min) were needed. Although, no changes were seen in 120 min images compared to 30 min images. Considering the used protocol, from the evaluated data it can be concluded that lymphoscintigraphy after 30 min periareolar injection of about 0.5-1 mCi 99mTc-Phytate in an intradermal volume of about 0.1 cc yields an axillary sentinel node in all the patients. Imaging 120 min after injection is of no additional value and can be omitted.
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