David Dagan Feng

Fundamentals of Content-Based Image Retrieval

We introduce in this chapter some fundamental theories for content-based image retrieval. Section 1.1 looks at the development of content-based image retrieval techniques. Then, as the emphasis of this chapter, we introduce in detail in Section 1.2 some widely used methods for visual content descriptions. After that, we briefly address similarity/distance measures between visual features, the indexing schemes, query formation, relevance feedback, and system performance evaluation in Sections 1.3, 1.4 and 1.5. Details of these techniques are discussed in subsequent chapters. Finally, we draw a conclusion in Section 1.6.

In-Shoe Plantar Pressure Measurement and Analysis System Based on Fabric Pressure Sensing Array

Spatial and temporal plantar pressure distributions are important and useful measures in footwear evaluation, athletic training, clinical gait analysis, and pathology foot diagnosis. However, present plantar pressure measurement and analysis systems are more or less uncomfortable to wear and expensive. This paper presents an in-shoe plantar pressure measurement and analysis system based on a textile fabric sensor array, which is soft, light, and has a high-pressure sensitivity and a long service life. The sensors are connected with a soft polymeric board through conductive yarns and integrated into an insole. A stable data acquisition system interfaces with the insole, wirelessly transmits the acquired data to remote receiver through Bluetooth path. Three configuration modes are incorporated to gain connection with desktop, laptop, or smart phone, which can be configured to comfortably work in research laboratories, clinics, sport ground, and other outdoor environments. A real-time display and analysis software is presented to calculate parameters such as mean pressure, peak pressure, center of pressure (COP), and shift speed of COP. Experimental results show that this system has stable performance in both static and dynamic measurements.

A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study

Positron emission tomography (PET) is an important tool for enabling quantification of human brain function. However, quantitative studies using tracer kinetic modeling require the measurement of the tracer time-activity curve in plasma (PTAC) as the model input function. It is widely believed that the insertion of arterial lines and the subsequent collection and processing of the biomedical signal sampled from the arterial blood are not compatible with the practice of clinical PET, as it is invasive and exposes personnel to the risks associated with the handling of patient blood and radiation dose. Therefore, it is of interest to develop practical noninvasive measurement techniques for tracer kinetic modeling with PET. In this paper, a technique is proposed to extract the input function together with the physiological parameters from the brain dynamic images alone. The identifiability of this method is tested rigorously by using Monte Carlo simulation. The results show that the proposed method is able to quantify all the required parameters by using the information obtained from two or more regions of interest (ROIs) with very different dynamics in the PET dynamic images. There is no significant improvement in parameter estimation for the local cerebral metabolic rate of glucose (LCMRGlc) if there are more than three ROIs. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study.

Content-based retrieval of dynamic PET functional images

The recent information explosion has led to a massively increased demand for multimedia data storage in integrated database systems. Content-based retrieval is an important alternative and complement to traditional keyword-based searching for multimedia data and can greatly enhance information management. However, current content-based image retrieval techniques have some deficiencies when applied in the biomedical functional imaging domain. In this paper, we presented a prototype design for a content-based functional image retrieval database system for dynamic positron emission tomography (PET). The system supports efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. This design makes it possible to maintain a large number of patient data sets online and to rapidly retrieve dynamic functional image sequences for the interpretation and generation of physiological parametric images, and offers potential advantages in medical image data management and telemedicine, as well as providing possible opportunities in the statistical and comparative analysis of functional image data.

Morphology-based multifractal estimation for texture segmentation

Multifractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based multifractal dimension estimations are most commonly used. However, in spite of its computational efficiency, the regular partition scheme used by various box-counting methods intrinsically produces less accurate results. In this paper, a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors, namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures. A series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture mosaics and real images. The comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations.

Feature-Based Image Patch Approximation for Lung Tissue Classification

In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.

Simultaneous estimation of physiological parameters and the input function - in vivo PET data

Dynamic imaging with positron emission tomography (PET) is widely used for the in-vivo measurement of the regional cerebral metabolic rate for glucose (rCMRGlc) with [/sup 18/F]fluorodeoxy-D-glucose (FDG), and is used for the clinical evaluation of neurological diseases. However, in addition to the acquisition of dynamic images, continuous arterial blood sampling is the conventional method of obtaining the tracer time-activity curve in blood (or plasma) for the numerical estimation of rCMRGlc in mg glucose/100 g tissue/min. The insertion of arterial lines and the subsequent collection and processing of multiple blood samples are impractical for clinical PET studies because it is invasive, it has the remote (but real) potential for producing limb ischemia, and it exposes personnel to additional radiation and the risks associated with handling blood. Based on a method for extracting kinetic parameters from dynamic PET images, we developed a modified version (post-estimation method) to improve the numerical identifiability of the parameter estimates when we deal with data obtained from clinical studies. We applied both methods to dynamic neurological FDG PET studies in three adults. We found that the input function and parameter estimates obtained with our noninvasive methods agreed well with those estimated from the gold-standard method of arterial blood sampling and that rCMRGlc estimates were highly correlated. No significant difference was found between rCMRGlc estimated by our methods and the gold-standard method. We suggest that our proposed noninvasive methods may offer an advance over existing methods.

Image segmentation by clustering of spatial patterns

This letter describes an approach to perceptual segmentation of images through the means of clustering of spatial patterns. An image is modeled as a set of spatial patterns defined on a rectangular lattice. The distance between a spatial pattern and each cluster is defined as a combination of the Euclidean distance in the feature space and the spatial dissimilarity which reflects how much of the patterns neighbourhood is occupied by other clusters. Our approach has been compared with the Fuzzy C-Mean (FCM) algorithm, a spatial fuzzy clustering algorithm and a Markov Random Field (MRF) based algorithm by segmenting synthetic images, texture mosaics and natural images. The results of those comparative experiments demonstrate that the proposed approach can segment images more effectively and provide more robust segmentation results.

Attention-driven image interpretation with application to image retrieval

Visual attention, a selective procedure of humans early vision, plays a very important role for humans to understand a scene by intuitively emphasizing some focused regions/objects. Being aware of this, we propose an attention-driven image interpretation method that pops out visual attentive objects from an image iteratively by maximizing a global attention function. In this method, an image can be interpreted as containing several perceptually attended objects as well as a background, where each object has an attention value. The attention values of attentive objectives are then mapped to importance factors so as to facilitate the subsequent image retrieval. An attention-driven matching algorithm is proposed in this paper based on a retrieval strategy emphasizing attended objects. Experiments on 7376 Hemera color images annotated by keywords show that the retrieval results from our attention-driven approach compare favorably with conventional methods, especially when the important objects are seriously concealed by the irrelevant background.

Robust saliency detection via regularized random walks ranking

In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches.