Journal of medical and bioengineering 

About: Journal of medical and bioengineering is an academic journal. The journal publishes majorly in the area(s): Fermentation & Productivity. It has an ISSN identifier of 2301-3796. Over the lifetime, 154 publications have been published receiving 777 citations.

Level Set Segmentation of Optic Discs from Retinal Images

Abstract: Analysis of retinal images can provide important information for detecting and tracing retinal and vascular diseases. The purpose of this work is to design a method that can automatically segment the optic disc in the digital fundus images. The template matching method is used to approximately locate the optic disc centre, and the blood vessel is extracted to reset the centre. This is followed by applying the Level Set Method, which incorporates edge term, distance-regularization term and shape-prior term, to segment the shape of the optic disc. Seven measures are used to evaluate the performance of the methods. The effectiveness of the proposed method is evaluated against alternative methods on three public data sets DRIVE, DIARETDB1 and DIARETDB0. The results show that our method outperforms the state-of-the-art methods on these datasets.

In Vitro Propagation of an Edible Bamboo Bam- busa Bambos and Assessment of Clonal Fidelity through Molecular Markers

Abstract:  Abstract—An efficient and reproducible protocol has been established through the technique of forced axillary branching for the propagation of an important edible bamboo species namely Bambusa bambos. High frequency multiple shoot induction was achieved from nodal segments collected from elite genotype on Murashige and Skoog’s (MS) medium supplemented with 4.4 µM Benzylaminopurine (BAP) and 1.16 µM Kinetin (Kn). The size of explant and season greatly influenced the frequency of bud break. Rooting posed a major problem to be worked out in this particular species. Best rooting response was observed on 9.80 µM of Indole- 3 Butyric acid (IBA) with 60 ± 14.1 % rooting. In vitro raised plants were successfully acclimatized and established in the field conditions where they exhibited normal growth. In a bid to ascertain genetic fidelity, DNA was extracted by CTAB method and samples were analysed in 1.8% agarose gel electrophoresis. In the present study no variation was reported among the in vitro raised progeny and the mother plant in the banding profiles generated by the total of fifteen Random Amplified polymorphic DNA (RAPD) and Inter Simple Sequence Repeats (ISSR) markers. Hence, molecular analysis confirmed that these plants were genetically similar and can be used as elite plants.

Influence of Feature Selection on Naïve Bayes Classifier for Recognizing Patterns in Cardiotocograms

Abstract: Cardiotocography is a technical procedure that consists in recording the fetal heart rate (FHR) and uterine activity (UA) during the last months of a pregnancy. Cardiotocogram (CTG) analysis consists in identifying some patterns associated to fetal activity in order to detect potential fetal pathologies. Several automatic classification methods have been already tested on CTG data sets, while a few feature selection (FS) methods have been considered. The aim of this paper is to investigate the influence of FS on the performance of a naïve Bayes classifier for FHR patterns and fetal states. We empirically compare the performance of several models using four different FS methods (Correlation-based, ReliefF, Information Gain, and Mutual Information). We find that ReliefF yields to a better performance for fetal state classification, while no FS method worth the effort for FHR pattern classification. 

Peroxidase Activity in Native and Callus Culture of Moringa Oleifera Lam

Abstract: Manscript received May 13, 2013; revised July 11, 2013. Abstract—In vitro Moringa oleifera Lam. seedlings were successfully produced through seed culture on Murashige & Skoog (MS) agar medium containing 3% (w/v) sucrose and 0.2% (w/v) Gelrite in the absence of growth regulators under 1,500 lux of light density, 16 hour photoperiod light at temperature of 25 ± 2°C. Shoot-derived callus and rootderived callus of M. oleifera were established via culture of shoot and root on the MS medium supplemented with 0.5 mg/l of 2,4-dichlorophenoxy acetic acid (2,4-D) in the dark at 25 ± 2°C. Stem, leaf and root of native M. oleifera and M. oleifera callus were assayed for peroxidase activity using guaiacol as a substrate of the enzyme. In native plant, crude extract from root provided the highest peroxidase specific activity, followed by those from stem and leaf with the specific activity of 19.73 ± 0.18, 16.56 ± 1.43 and 13.38 ± 1.04 unit/mg protein, respectively. Crude extract of root-derived callus and shoot-derived callus of M. oleifera possessed specific activity of 167.25 ± 16.12 and 103.99 ± 10.64 unit/mg protein. These values are significantly higher than their counter parts from native M. oleifera suggesting the potential use of the callus cultures as new and improved sources of peroxidase.

Diagnosis of Coronary Artery Disease Using Data Mining Based on Lab Data and Echo Features

Abstract: According to American Heart Association report, cardiovascular diseases are one of the five leading causes of death in the world. Coronary Artery Disease (CAD) is the most common fatal heart disease, and is the subject of large body of studies. According to prevalence of CAD, early diagnosis of this disease is very important. The most reliable method for CAD diagnosis is angiography, but it is costly, time-consuming, and hazardous. Therefore in order to predict such diseases, study of non-invasive methods such as analysis and mining of patients' medical information is becoming popular, and has proved to be effective. Unfortunately, majority of approaches in the literature rely on a limited and small set of medical features for disease detection. This paper aims to examine effects of set of features; including lab data and echo information on CAD diagnosis which some of them were not considered in previous studies. The data set consists of the information gathered from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center which is one of the largest heart hospitals in Asia. The method used in this research was data mining. Several classification algorithms were adopted to analyze the data set, including SMO, Naive Bayes, C4.5 and AdaBoost. According to the comprehensive set of features used, the obtained classification accuracy exceeded 82 percent. Results showed that new added features including Region with RWMA and Ejection Fraction (EF) have a large effect on CAD.