This paper aims to tackle the Patient Admission Scheduling Problem (PASP) using the Discrete Flower Pollination Algorithm (DFPA), a new, meta-heuristic optimization method based on plant pollination. PASP is one of the most important problems in the field of health care. It is a highly constrained and combinatorial optimization problem of assigning patients to medical resources in a hospital, subject to predefined constraints, while maximizing patient comfort. While the flower pollination algorithm was designed for continuous optimization domains, a discretization of the algorithm has been carried out for application to the PASP. Various neighborhood structures have been employed to enhance the method, and to explore more solutions in the search space. The proposed method has been tested on six instances of benchmark datasets for comparison against another algorithm using the same dataset. The prospective method is shown to be very efficient in solving any scheduling problem.
External fixators have been widely used in treating open fractures and have produced excellent outcomes, as they could successfully heal bones. The stability of external fixators lies greatly in their construction. Factors that associated with the stability of the external fixators includes stress, displacement, and relative micromotion. Three-dimensional (3D) models of bone and external fixators were constructed by using 3D modelling software, namely Materialise and SolidWorks, respectively. Three different configurations of external fixators namely Model 1, Model 2, and Model 3 were analysed. Three load cases were simulated to assess the abovementioned factors at the bone, specifically at the fracture site and at the external fixator. Findings showed that the double-cross configuration (Model 3) was the most promising in axial, bending, and torsion load cases as compared to the other two configurations. The no-cross configuration (Model 1) had the highest risk of complication due to high stress, relative micromotion, and displacement in the bending and torsion load cases. On the other hand, the single-cross configuration (Model 2) had the highest risk of complication when applied with axial load. In conclusion, the double-cross locking construct (Model 3) showed the biggest potential to be a new option for medical surgeons in treating patients associated with bone fracture. This new double-cross locking construct showed superior biomechanical stability as compared to single-cross and no-cross configurations in the axial, bending, and torsion load cases.
Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians. However, manual screening is labour-intensive, tiresome and time consuming. Hence, there is a need to automate the eye screening. In this work Bi-dimensional Empirical Mode Decomposition (BEMD) technique is used to decompose fundus images into 2D Intrinsic Mode Functions (IMFs) to capture variations in the pixels due to morphological changes. Further, various entropy namely Renyi, Fuzzy, Shannon, Vajda, Kapur and Yager and energy features are extracted from IMFs. These extracted features are ranked using Chernoff Bound and Bhattacharyya Distance (CBBD), Kullback-Leibler Divergence (KLD), Fuzzy-minimum Redundancy Maximum Relevance (FmRMR), Wilcoxon, Receiver Operating Characteristics Curve (ROC) and t-test methods. Further, these ranked features are fed to Support Vector Machine (SVM) classifier to classify normal and abnormal (DR, AMD and glaucoma) classes. The performance of the proposed eye screening system is evaluated using 800 (Normal=400 and Abnormal=400) digital fundus images and 10-fold cross validation method. Our proposed system automatically identifies normal and abnormal classes with an average accuracy of 88.63%, sensitivity of 86.25% and specificity of 91% using 17 optimal features ranked using CBBD and SVM-Radial Basis Function (RBF) classifier. Moreover, a novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number. Such a system helps to reduce eye screening time in polyclinics or community-based mass screening. They will refer the patients to main hospitals only if the diagnosis belong to the abnormal class. Hence, the main hospitals will not be unnecessarily crowded and doctors can devote their time for other urgent cases.
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
An accurate detection of preterm labor and the risk of preterm delivery before 37 weeks of gestational age is crucial to increase the chance of survival rate for both mother and the infant. Thus, the uterine contractions measured using uterine electromyogram (EMG) or electro hysterogram (EHG) need to have high sensitivity in the detection of true preterm labor signs. However, visual observation and manual interpretation of EHG signals at the time of emergency situation may lead to errors. Therefore, the employment of computer-based approaches can assist in fast and accurate detection during the emergency situation. This work proposes a novel algorithm using empirical mode decomposition (EMD) combined with wavelet packet decomposition (WPD), for automated prediction of pregnant women going to have premature delivery by using uterine EMG signals. The EMD is performed up to 11 levels on the normal and preterm EHG signals to obtain the different intrinsic mode functions (IMFs). These IMFs are further subjected to 6 levels of WPD and from the obtained coefficients, eight different features are extracted. From these extracted features, only the significant features are selected using particle swarm optimization (PSO) method and selected features are ranked by Bhattacharyya technique. All the ranked features are fed to support vector machine (SVM) classifier for automated differentiation and achieved an accuracy of 96.25%, sensitivity of 95.08%, and specificity of 97.33% using only ten EHG signal features. Our proposed algorithm can be used in gynecology departments of hospitals to predict the preterm or normal delivery of pregnant women.
The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.
Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
Shear wave elastography (SWE) examination using ultrasound elastography (USE) is a popular imaging procedure for obtaining elasticity information of breast lesions. Elasticity parameters obtained through SWE can be used as biomarkers that can distinguish malignant breast lesions from benign ones. Furthermore, the elasticity parameters extracted from SWE can speed up the diagnosis and possibly reduce human errors. In this paper, Shearlet transform and local binary pattern histograms (LBPH) are proposed as an original algorithm to differentiate malignant and benign breast lesions. First, Shearlet transform is applied on the SWE images to acquire low frequency, horizontal and vertical cone coefficients. Next, LBPH features are extracted from the Shearlet transform coefficients and subjected to dimensionality reduction using locality sensitivity discriminating analysis (LSDA). The reduced LSDA components are ranked and then fed to several classifiers for the automated classification of breast lesions. A probabilistic neural network classifier trained only with seven top ranked features performed best, and achieved 98.08% accuracy, 98.63% sensitivity, and 97.59% specificity in distinguishing malignant from benign breast lesions. The high sensitivity and specificity of our system indicates that it can be employed as a primary screening tool for faster diagnosis of breast malignancies, thereby possibly reducing the mortality rate due to breast cancer.
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.
This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.
Diabetes mellitus (DM) is a chronic metabolic disorder that requires regular medical care to prevent severe complications. The elevated blood glucose level affects the eyes, blood vessels, nerves, heart, and kidneys after the onset. The affected blood vessels (usually due to atherosclerosis) may lead to insufficient blood circulation particularly in the lower extremities and nerve damage (neuropathy), which can result in serious foot complications. Hence, an early detection and treatment can prevent foot complications such as ulcerations and amputations. Clinicians often assess the diabetic foot for sensory deficits with clinical tools, and the resulting foot severity is often manually evaluated. The infrared thermography is a fast, nonintrusive and non-contact method which allows the visualization of foot plantar temperature distribution. Several studies have proposed infrared thermography-based computer aided diagnosis (CAD) methods for diabetic foot. Among them, the asymmetric temperature analysis method is more superior, as it is easy to implement, and yielded satisfactory results in most of the studies. In this paper, the diabetic foot, its pathophysiology, conventional assessments methods, infrared thermography and the different infrared thermography-based CAD analysis methods are reviewed.
Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
Monitoring FAZ area enlargement enables physicians to monitor progression of the DR. At present, it is difficult to discern the FAZ area and to measure its enlargement in an objective manner using digital fundus images. A semi-automated approach for determination of FAZ using color images has been developed. Here, a binary map of retinal blood vessels is computer generated from the digital fundus image to determine vessel ends and pathologies surrounding FAZ for area analysis. The proposed method is found to achieve accuracies from 66.67% to 98.69% compared to accuracies of 18.13-95.07% obtained by manual segmentation of FAZ regions from digital fundus images.
An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.
The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods.
Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.