Academic program models for undergraduate biomedical engineering.

There is a proliferation of medical devices across the globe for the diagnosis and therapy of diseases. Biomedical engineering (BME) plays a significant role in healthcare and advancing medical technologies thus creating a substantial demand for biomedical engineers at undergraduate and graduate levels. There has been a surge in undergraduate programs due to increasing demands from the biomedical industries to cover many of their segments from bench to bedside. With the requirement of multidisciplinary training within allottable duration, it is indeed a challenge to design a comprehensive standardized undergraduate BME program to suit the needs of educators across the globe. This paper's objective is to describe three major models of undergraduate BME programs and their curricular requirements, with relevant recommendations to be applicable in institutions of higher education located in varied resource settings. Model 1 is based on programs to be offered in large research-intensive universities with multiple focus areas. The focus areas depend on the institution's research expertise and training mission. Model 2 has basic segments similar to those of Model 1, but the focus areas are limited due to resource constraints. In this model, co-op/internship in hospitals or medical companies is included which prepares the graduates for the work place. In Model 3, students are trained to earn an Associate Degree in the initial two years and they are trained for two more years to be BME's or BME Technologists. This model is well suited for the resource-poor countries. All three models must be designed to meet applicable accreditation requirements. The challenges in designing undergraduate BME programs include manpower, facility and funding resource requirements and time constraints. Each academic institution has to carefully analyze its short term and long term requirements. In conclusion, three models for BME programs are described based on large universities, colleges, and community colleges. Model 1 is suitable for research-intensive universities. Models 2 and 3 can be successfully implemented in higher education institutions with low and limited resources with appropriate guidance and support from international organizations. The models will continually evolve mainly to meet the industry needs.

Editorial comment on Malkin and Keane (2010).

Malkin and Keane (Med Biol Eng Comput, 2010) take an innovative approach to determine if unused, broken medical and laboratory equipment could be repaired by volunteers with limited resources. Their positive results led them to suggest that resource-poor countries might benefit from an on-the-job educational program for local high school graduates. The program would train biomedical technician assistants (BTAs) who would repair medical devices and instrumentation and return them to service. This is a program worth pursuing in resource-poor countries.

Incorporating temporal information into active contour method for detecting heart wall boundary from echocardiographic image sequence.

A new method which incorporates temporal information into the active contour function is proposed to solve the dropout and speckle noise problems encountered when detecting the inner heart wall boundary from echocardiographic image sequence. The ventricular boundary is considered to be composed of strong and weak segments. The weak segments are interfered by image degradations in ultrasound, and they are too weak to constrain the curve evolution. Temporal information is incorporated into the external energy of the active contour function to recover the missing boundary and strengthen the weak segments. Experimental results demonstrate the advantages of the proposed method for the intended task.

Watershed-presegmented snake for boundary detection and tracking of left ventricle in echocardiographic images.

In this paper, an automated method of boundary detection of the left ventricle (LV) is proposed. The method uses a watershed transform and morphological operation to locate the region containing the LV, then performs snake deformation with a multiscale directional edge map for the detection of the endocardial boundary of the LV.

Feature extraction for the analysis of colon status from the endoscopic images.

BACKGROUND: Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS: Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN). RESULTS: Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (tau = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, tau = 1%) or color (90.52%, tau = 1%) features. CONCLUSION: In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method.

Cardiac arrhythmia classification using autoregressive modeling.

BACKGROUND: Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac arrhythmias including atrial premature contraction (APC), premature ventricular contraction (PVC), superventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation (VF). METHODS: AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM) based algorithm in various stages. RESULTS: AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. CONCLUSION: The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.

Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter.

In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the "spreading factor" of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection.