BIO MEDICAL ENGINEERING
BIO MEDICAL ENGINEERING
1.A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration
Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in clinical trials. We first developed and applied a multi-resolution B-spline based deformable image registration method to register two sets of computed tomography (CT) image data acquired pre- and post-treatment. The B-spline difference maps generated from the co-registered CT images highlight the regions related to the volumetric growth or shrinkage of the metastatic tumors, and density changes related to variation of necrosis inside the solid tumors. Using a testing dataset involving 19 ovarian cancer patients, we compared patients’ response to the treatment using the new image registration method and RECIST guideline. The results demonstrated that using the image registration method yielded higher association with the six-month PFS outcomes of the patients than using RECIST. The image registration results also provided a solid foundation of developing new computerized quantitative image feature analysis schemes in the future studies
2.Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology
Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images by extracting hundreds of image features and using them to predict disease presence or outcome. Since constructing a robust and interpretable classifier is challenging in a high dimensional feature space, dimensionality reduction (DR) is often implemented prior to classifier construction. However, when DR is performed it can be challenging to quantify the contribution of each of the original features to the final classification result. We have previously presented a method for scoring features based on their importance for classification on an embedding derived via principal components analysis (PCA). However, nonlinear DR involves the eigen-decomposition of a kernel matrix rather than the data itself, compounding the issue of classifier interpretability. In this paper we present feature importance in nonlinear embeddings (FINE), an extension of our PCA-based feature scoring method to kernel PCA (KPCA), as well as several NLDR algorithms that can be cast as variants of KPCA. FINE is applied to four digital pathology datasets to identify key QH features for predicting the risk of breast and prostate cancer recurrence. Measures of nuclear and glandular architecture and clusteredness were found to play an important role in predicting the likelihood of recurrence of both breast and prostate cancers. Compared to the t-test, Fisher score, and Gini index, FINE was able to identify a stable set of features that provide good classification accuracy on four publicly available datasets from the NIPS 2003 Feature Selection Challenge.
3.Image Registration Based on Autocorrelation of Local Structure
Registration of images in the presence of intra-image signal fluctuations is a challenging task. The definition of an appropriate objective function measuring the similarity between the images is crucial for accurate registration. This paper introduces an objective function that embeds local phase features derived from the monogenic signal in the modality independent neighborhood descriptor (MIND). The image similarity relies on the autocorrelation of local structure (ALOST) which has two important properties: 1) low sensitivity to space-variant intensity distortions (e.g., differences in contrast enhancement in MRI); 2) high distinctiveness for ‘salient’ image features such as edges. The ALOST method is quantitatively compared to the MIND approach based on three different datasets: thoracic CT images, synthetic and real abdominal MR images. The proposed method outperformed the NMI and MIND similarity measures on these three datasets. The registration of dynamic contrast enhanced and post-contrast MR images of patients with Crohn’s disease led to relative contrast enhancement measures with the highest correlation to the Crohn’s disease endoscopic index of severity
4.Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation
Edge-based active contourmodels are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed fromany classification algorithm and applied to any edge-based model using a level set method. Experiments onmedical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbours and the support vector machine confirm the effectiveness of the proposed approach.
5.How Divided is a Cell? Eigenphase Nuclei for Classification of Mitotic Phase in Cancer Histology Images
Detection of mitotic cells in histology images is an important but challenging process due to the resemblance of mitotic cells with other non-mitotic cells and also due to the different appearance of mitotic cells undergoing different phases of the division process. In this paper, we present an algorithm for classification of mitotic cells into its four different phases using eigenphase nuclei images – nuclear exemplars obtained separately from the eigen-decomposition of training nuclei images belonging to each of the four mitotic phases. To the best of our knowledge, ours is the first method to identify mitotic phases in cancer histology images. It is quite likely that the classification results may be negatively affected if the dataset used for training purposes does not contain sufficient number of samples for a positive class. To overcome this class imbalance problem, we present a novel method for oversampling the minority class. The proposed method generates synthetic images for training purposes by perturbing the representation of training samples belonging to the minority class in the eigenphase domain. We show that this strategy works effectively for pairwise classification of the mitotic cells – increasing the classification performance by as much as 24%.
6.Novel Accurate and Fast Optic Disc Detection in Retinal Images with Vessel Distribution and Directional Characteristics
A novel accurate and fast optic disc (OD) detection method is proposed by using vessel distribution and directional characteristics. A feature combining 3 vessel distribution characteristics, i.e. local vessel density, compactness and uniformity, is designed to find possible horizontal coordinate of OD. Then according to the global vessel direction characteristic, a General Hough Transformation (GHT) is introduced to identify the vertical coordinate of OD. By confining the possible OD vertical range and by simplifying vessel structure with blocks, we greatly decrease the computational cost of the algorithm. Four public datasets have been tested. The OD localization accuracy lies from 93.8% to 99.7%, when 8%~20% vessel detection results are adopted to achieve OD detection. Average computation times for STARE images are about 3.4-11.5s, which relate to image size. The proposed method shows satisfactory robustness on both normal and diseased images. It is better than many previous methods with respect to accuracy and efficiency
7.Visualization of Tumor Response to Neoadjuvant Therapy for Rectal Carcinoma by Nonlinear Optical Imaging
The continuing development of nonlinear optical imaging techniques has opened many new windows in biological exploration. In this study, a nonlinear optical microscopy— multiphoton microscopy (MPM) was expanded to detect tumor response in rectal carcinoma after neoadjuvant therapy; especially normal tissue, pre- and post-therapeutic cancerous tissues were investigated in order to present more detailed information and make comparison. It was found that the MPM has ability not only to directly visualize histopathologic changes in rectal carcinoma, including stromal fibrosis, colloid response, residual tumors, blood vessel hyperplasia, and inflammatory reaction, which had been proven to have important influence on estimation of the prognosis and the effect of neoadjuvant treatment, but also to provide quantitative optical biomarkers including the intensity ratio of SHG over TPEF and collagen orientation index. These results show that the MPM will become a useful tool for clinicians to determine whether neoadjuvant therapy is effective or treatment strategy is approximate, and this study may provide the groundwork for further exploration into the application of MPM in a clinical setting.
8.Discriminative Feature Extraction from X-ray Images using Deep Convolutional Neural Networks
Feature extraction is one of the most important phases of medical image classification which requires extensive domain knowledge. Convolutional Neural Networks (CNN) have been successfully used for feature extraction in images from different domains involving a lot of classes. In this paper, CNNs are exploited to extract a hierarchical and discriminative representation of X-ray images. This representation is then used for classification of the X-ray images as various parts of the body. Visualization of the feature maps in the hidden layers show that features learnt by the CNN resemble the essential features which help discern the discrimination among different body parts. A comparison on the standard IRMA X-ray image dataset demonstrates that the CNNs easily outperform classifiers
with hand-engineered features..
9. A Circuit Model of Real Time Human Body Hydration
A Circuit Model of Real Time Human Body Hydration
Changes in human body hydration leading to excess fluid losses or overload affects the body fluid’s ability to provide the necessary support for healthy living. We propose a time dependent circuit model of real time human body hydration, which models the human body tissue as a signal transmission medium. The circuit model predicts the attenuation of a propagating electrical signal. Hydration rates are modeled by a time constant _ which characterizes the individual specific metabolic function of the body part measured. We define a surrogate human body anthropometric parameter _ by the muscle-fat ratio and comparing it with the Body Mass Index (BMI), we find theoretically, the rate of hydration varying from 1.73 dB/minute, for high _ and low _ to 0.05 dB/minute for low _ and high _ . We compare these theoretical values with empirical measurements and show that real time changes in human body hydration can be observed by measuring signal attenuation. We took empirical measurements using a vector network analyser and obtained different hydration rates for various BMI, ranging from 0.6 dB/minute for 22.7 kg=m2 down to 0.04 dB/minute for 41.2 kg=m2. We conclude that the galvanic coupling circuit model can predict changes in the volume of the body fluid which are essential in diagnosing and monitoring treatment of body fluid disorder. Individuals with high BMI would have higher time-dependent biological characteristic, lower metabolic rate and lower rate of hydration.
10.Estimation of Circadian Body Temperature Rhythm based on Heart Rate in Healthy, Ambulatory Subjects
11.Smart Belt: A wearable device for managing abdominal obesity
Smart Belt: A wearable device for managing abdominal obesity
It is well-known that an improper lifestyle such as overeating, lack of exercises and imbalance of body postures is regarded as the main cause of abdominal obesity which could cause various complications such as high blood pressure, diabetes, heart failure, and so on. There have been tremendous efforts to improve such a lifestyle and reduce abdominal obesity. One of the results suggests that a correct posture could lead the reduction of the abdominal obesity. However, it is difficult for users to measure, record and correct their postures by themselves. Therefore, it is required to have a tool to enhance the situation. It is assumed that wearable devices can be used for supporting the efforts of reducing the abdominal obesity. In this paper, it is proposed to develop a wearable device, called Smart Belt, for life logging, monitoring and analyzing living body data with personal big data processing.
12. A Multi-Signal Acquisition System for Preventive Cardiology with Cuff-less BP Measurement Capability
A Multi-Signal Acquisition System for Preventive Cardiology with Cuff-less BP Measurement Capability
Remarkable technological developments in recent years have opened up new possibilities of improving public health care system. Developments in computing, signal processing and communication technologies have resulted in new diagnostic instruments with better precision and connectivity with computing machines, communication gadgets and databases. However, many of these technical marvels are young by age and not yet available to all potential users at affordable cost. Among the various vital biomedical signals that can be collected noninvasively, ECG, PPG, SpO2 and PCG collected simultaneously, can help in examining electrical (ECG) and mechanical (PCG) functioning of heart, efficiency of lungs (SpO2) and condition of arteries (PPG). This promises to give a composite overview of the whole cardiovascular system non-invasively. We propose to develop a portable multi-parameter, non-invasive biomedical signal acquisition system (which can monitor, store and communicate the above mentioned four biomedical signals) for regular telemonitoring of patients especially with cardiovascular complaints. The device will be easy to use and can be operated by a paramedical staff with minimal training. It will be low-cost, so that a large population can be covered using multiple portable units. The gathered physiological data can be sent to the monitoring station and depending on the condition of the patient, further instructions can be issued from there. We also propose to analyze the different signals gathered and find correlation present in them which can lead to a better understanding of the whole cardiovascular system and assist doctors in early diagnosis of crucial symptoms. As an initial step towards that, Blood pressure (BP) and Heart rate (HR) is monitored using the gathered ECG and PPG signals from the device. The reliability of the device for BP and HR monitoring is estimated by comparing it with a clinically proven professional automatic digital BP monitor (OMRON HBP1300).