In this paper we propose to use the Wavelet Leader (WL) transformation for studying trabecular bone patterns. Given an input image, its WL transformation is defined as the cross-channel-layer maximum pooling of an underlying wavelet transformation. WL inherits the advantage of the original wavelet transformation in capturing spatial-frequency statistics of texture images, while being more robust against scale and orientation thanks to the maximum pooling strategy. These properties make WL an attractive alternative to replace wavelet transformations which are used for trabecular analysis in previous studies. In particular, in this paper, after extracting wavelet leader descriptors from a trabecular texture patch, we feed them into two existing statistic texture characterization methods, namely the Gray Level Co-occurrence Matrix (GLCM) and the Gray Level Run Length Matrix (GLRLM). The most discriminative features, Energy of GLCM and Gray Level Non-Uniformity of GLRLM, are retained to distinguish two different populations between osteoporotic patients and control subjects. Receiver Operating Characteristics (ROC) curves are used to measure performance of classification. Experimental results on a recently released benchmark dataset show that WL significantly boosts the performance of baseline wavelet transformations by 5% in average.
Osteoporosis is the common cause for a broken bone among senior citizens. Early diagnosis of osteoporosis requires routine examination which may be costly for patients. A potential low cost diagnosis is to identify a senior citizen at high risk of osteoporosis by pre-screening during routine dental examination. Therefore, osteoporosis analysis using dental radiographs severs as a key step in routine dental examination. The aim of this study is to localize landmarks in dental radiographs which are helpful to assess the evidence of osteoporosis. We collect eight landmarks which are critical in osteoporosis analysis. Our goal is to localize these landmarks automatically for a given dental radiographic image. To address the challenges such as large variations of appearances in subjects, in this paper, we formulate the task into a multi-class classification problem. A hybrid feature pool is used to represent these landmarks. For the discriminative classification problem, we use a random forest to fuse the hybrid feature representation. In the experiments, we also evaluate the performances of individual feature component and the hybrid fused feature. Our proposed method achieves average detection error of 2:9mm.
The objective of this work is to indicate a monitoring methodology in order to survey the present state of the quarry sites and their evolution in time, which are the basic data needed to implement an adequate land reclamation project. The land monitoring has been realised by UAV photogrammetry and GPS measurements supported by a Geographic Information System. A six-rotor aircraft with a total weight of 6 kg carrying two small cameras has been used. Very accurate digital airphotos have been used in order to create orthophotos mosaic and DSM from the quarry planes. DGPS measurements and the data captured from the UAV are combined in GIS and the results are presented in the current study.
The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic
detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases.
In this paper we propose an automatic method for periapical root region detection by using the state-of-theart
machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root
detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this
problem, we boost the training set by including more root regions that are close to the annotated ones and
decompose the original images to randomly generate negative samples. Based on these training samples,
the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root
detector. The learned detector usually generates a large amount of true and false positives. In order to
reduce the number of false positives, a confidence score for each candidate detection result is calculated for
further purification. We first merge the detected regions by combining tightly overlapped detected
candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false
positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray
images from 21 patients. The experimental results show that our approach can achieve promising detection
accuracy.
Periapical lesion is a common disease in oral health. While many studies have been devoted to image-based
diagnosis of periapical lesion, these studies usually require clinicians to perform the task. In this paper we
investigate the automatic solutions toward periapical lesion classification using quantized texture analysis.
Specifically, we adapt the bag-of-visual-words model for periapical root image representation, which
captures the texture information by collecting local patch statistics. Then we investigate several similarity
measure approaches with the K-nearest neighbor (KNN) classifier for the diagnosis task. To evaluate these
classifiers we have collected a digitized oral X-Ray image dataset from 21 patients, resulting 139 root
images in total. The extensive experimental results demonstrate that the KNN classifier based on the bagof-
words model can achieve very promising performance for periapical lesion classification.
This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching
structures with the underlying image texture. Morphological variability of the breast ductal network is associated with
subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia.
In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for
analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with
texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to
capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in
galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the
Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi
automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and
graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the
proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to
other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has
potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast
anatomy phantoms.
This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the
underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in
development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated
machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic
image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with
Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used
for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were
generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate
ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for
elimination of false positives. The strategies differed in their approach to combining confidence scores by summation,
averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with
annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation
strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of
maximum score showed the best performance with 96% detection rate.
Since several lung diseases can be potentially diagnosed based on the patterns of lung tissue observed in medical images,
automated texture classification can be useful in assisting the diagnosis. In this paper, we propose a methodology for
discriminating between various types of normal and diseased lung tissue in computed tomography (CT) images that
utilizes Vector Quantization (VQ), an image compression technique, to extract discriminative texture features. Rather
than focusing on images of the entire lung, we direct our attention to the extraction of local descriptors from individual
regions of interest (ROIs) as determined by domain experts. After determining the ROIs, we generate "locally optimal"
codebooks representing texture features of each region using the Generalized Lloyd Algorithm. We then utilize the
codeword usage frequency of each codebook as a discriminative feature vector for the region it represents. We compare
k-nearest neighbor, support vector machine and neural network classification approaches using the normalized
histogram intersection as a similarity measure. The classification accuracy reached up to 98% for certain experimental
settings, indicating that our approach may potentially assist clinicians in the interpretation of lung images and facilitate
the investigation of relationships among structure, texture and function or pathology related to several lung diseases.
KEYWORDS: Shape analysis, Control systems, Medical imaging, Detection and tracking algorithms, Stars, Image analysis, Brain, Medical diagnostics, Magnetic resonance imaging, Information science
In this work, we introduce a new representation technique of 2D contour shapes and a sequence similarity measure to
characterize 2D regions of interest in medical images. First, we define a distance function on contour points in order to
map the shape of a given contour to a sequence of real numbers. Thus, the computation of shape similarity is reduced to
the matching of the obtained sequences. Since both a query and a target sequence may be noisy, i.e., contain some outlier
elements, it is desirable to exclude the outliers in order to obtain a robust matching performance. For the computation of
shape similarity, we propose the use of an algorithm which performs elastic matching of two sequences. The contribution
of our approach is that, unlike previous works that require images to be warped according to a template image for
measuring their similarity, it obviates this need, therefore it can estimate image similarity for any type of medical image
in a fast and efficient manner. To demonstrate our method's applicability, we analyzed a brain image dataset consisting
of corpus callosum shapes, and we investigated the structural differences between children with chromosome 22q11.2
deletion syndrome and controls. Our findings indicate that our method is quite effective and it can be easily applied on
medical diagnosis in all cases of which shape difference is an important clue.
We propose a methodological framework for texture analysis in medical images that is based on Vector Quantization
(VQ), a method traditionally used for image compression. In this framework, the codeword usage histogram is used as a
texture descriptor of the image. This descriptor can be used effectively for similarity searches, clustering, classification
and other retrieval operations. We present an application of this approach to the analysis of x-ray galactograms; we
analyze the texture in retroareolar regions of interests (ROIs) in order to distinguish between patients with reported
galactographic findings and normal subjects. We decompose these ROIs into equi-size blocks and use VQ to represent
each block with the closest codeword from a codebook. Each image is represented as a vector of frequencies of
codeword appearance. We perform k-nearest neighbor classification of the texture patterns employing the histogram
model as a similarity measure. The classification accuracy reached up to 96% for certain experimental settings; these
results demonstrate that the proposed approach can be effective in performing similarity analysis of texture patterns in
breast imaging. The proposed texture analysis framework has a potential to assist the interpretation of clinical images in
general and facilitate the investigation of relationships among structure, texture and function or pathology.
We study the problem of classifying brain tumors as benign or malignant using information from magnetic resonance (MR) imaging and magnetic resonance spectroscopy (MRS) to assist in clinical diagnosis. The proposed approach consists of several steps including segmentation, feature extraction, feature selection, and classification model construction. Using an automated segmentation technique based on fuzzy connectedness we accurately outline the tumor mass boundaries in the MR images so that further analysis concentrates on these regions of interest (ROIs). We then apply a concentric circle technique on the ROIs to extract features that are utilized by the classification algorithms. To remove redundant features, we perform feature selection where only those features with discriminatory information (among classes) are used in the model building process. The involvement of MRS features further improves the classification accuracy of the model. Experimental results demonstrate the effectiveness of the proposed approach in classifying brain tumors in MR images.
We propose a multi-step approach for representing and classifying tree-like structures in medical images. Examples of such tree-like structures are encountered in the bronchial system, the vessel topology and the breast ductal network. We assume that the tree-like structures are already segmented. To avoid the tree isomorphism problem we obtain the breadth-first canonical form of a tree. Our approach is based on employing tree encoding techniques, such as the depth-first string encoding and the Prüfer encoding, to obtain a symbolic representation. Thus, the problem of classifying trees is reduced to string classification where node labels are the string terms. We employ the tf-idf text mining technique to assign a weight of significance to each string term (i.e., tree node label). We perform similarity searches and k-nearest neighbor classification of the trees using the tf-idf weight vectors and the cosine similarity metric. We applied our approach to the breast ductal network manually extracted from clinical x-ray galactograms. The goal was to characterize the ductal tree-like parenchymal structures in order to distinguish among different groups of women. Our best classification accuracy reached up to 90% for certain experimental settings (k=4), outperforming on the average by 10% that of a previous state-of-the-art method based on ramification matrices. These results illustrate the effectiveness of the proposed approach in analyzing tree-like patterns in breast images. Developing such automated tools for the analysis of tree-like structures in medical images can potentially provide insight to the relationship between the topology of branching and function or pathology.
KEYWORDS: Image segmentation, Single photon emission computed tomography, 3D image processing, Image analysis, Stomach, Visualization, Medical imaging, Image processing, 3D metrology, 3D image reconstruction
We have developed semi-automated and fully-automated tools for the analysis of 3D single-photon emission computed tomography (SPECT) images. The focus is on the efficient boundary delineation of complex 3D structures that enables accurate measurement of their structural and physiologic properties. We employ intensity based thresholding algorithms for interactive and semi-automated analysis. We also explore fuzzy-connectedness concepts for fully automating the segmentation process. We apply the proposed tools to SPECT image data capturing variation of gastric accommodation and emptying. These image analysis tools were developed within the framework of a noninvasive scintigraphic test to measure simultaneously both gastric emptying and gastric volume after ingestion of a solid or a liquid meal. The clinical focus of the particular analysis was to probe associations between gastric accommodation/emptying and functional dyspepsia. Employing the proposed tools, we outline effectively the complex three dimensional gastric boundaries shown in the 3D SPECT images. We also perform accurate volume calculations in order to quantitatively assess the gastric mass variation. This analysis was performed both with the semi-automated and fully-automated tools. The results were validated against manual segmentation performed by a human expert. We believe that the development of an automated segmentation tool for SPECT imaging of the gastric volume variability will allow for other new applications of SPECT imaging where there is a need to evaluate complex organ function or tumor masses.
In this paper, we introduce a new clustering algorithm, FCC, for intrusion detection based on the concept of fuzzy connectedness. This concept was introduced by Rosenfeld in 1979 and used with success in image segmentation; here we extend this approach to clustering and demonstrate its effectiveness in intrusion detection. Starting with a single or a few seed points in each cluster, all the data points are dynamically assigned to the cluster that has the highest fuzzy connectedness value (strongest connection). With an efficient heuristic algorithm, the time complexity of the clustering process is O(NlogN), where N is the number of data points. The value of fuzzy connectedness is calculated using both the Euclidean distance and the statistical properties of clusters. This unsupervised learning method allows the discovery of clusters of any shape. Application of the method in intrusion detection demonstrates that it can detect not only known intrusion types, but also their variants. Experimental results on the KDD-99 intrusion detection data set show the efficiency and accuracy of this method. A detection rate above 94% and a false alarm rate below 4% are achieved, outperforming major competitors by at least 5%.
KEYWORDS: Medical imaging, Feature extraction, Functional magnetic resonance imaging, Optical spheres, Brain, Image processing, 3D image processing, Tumors, Magnetic resonance imaging, Classification systems
We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.
We propose partitioning-based methods to facilitate the classification of 3-D binary image data sets of regions of interest (ROIs) with highly non-uniform distributions. The first method is based on recursive dynamic partitioning of a 3-D volume into a number of 3-D hyper-rectangles. For each hyper-rectangle, we consider, as a potential attribute, the number of voxels (volume elements) that belong to ROIs. A hyper-rectangle is partitioned only if the corresponding attribute does not have high discriminative power, determined by statistical tests, but it is still sufficiently large for further splitting. The final discriminative hyper-rectangles form new attributes that are further employed in neural network classification models. The second method is based on maximum likelihood employing non-spatial (k-means) and spatial DBSCAN clustering algorithms to estimate the parameters of the underlying distributions. The proposed methods were experimentally evaluated on mixtures of Gaussian distributions, on realistic lesion-deficit data generated by a simulator conforming to a clinical study, and on synthetic fractal data. Both proposed methods have provided good classification on Gaussian mixtures and on realistic data. However, the experimental results on fractal data indicated that the clustering-based methods were only slightly better than random guess, while the recursive partitioning provided significantly better classification accuracy.
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