Computer-aided detection (CAD) is a computerized procedure in medical science that supports the medical team's interpretations and decisions. CAD often uses information from a medical imaging modality such as Computed Tomography to detect suspicious lesions. Algorithms to detect these lesions are based on geometric models which can describe the local structures and thus provide potential region candidates. Geometrical descriptive models are very dependent on the data quality which may affect the false positive rates in CAD. In this paper we propose an efficient adaptive diffusion technique that adaptively controls the diffusion flux of the local structures in the data using robust statistics. The proposed method acts isotropically in the homogeneous regions and anisotropically in the vicinity of jump discontinuities. This method structurally enhances the data and makes the geometrical descriptive models robust. For the iterative solver, we use an efficient gradient descent flow solver based on a PDE formulation of the problem. The whole proposed strategy, which makes use of adaptive diffusion filter coupled with gradient descent flows has been developed and evaluated on clinical data in the application to colonic polyp detection in Computed Tomography Colonography.
Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human
anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This
paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm
combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in
a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on
the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is
integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich
information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been
evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each
segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio
over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results
demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular
and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities
between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other
anatomies, such as polyps in the colon.
This paper presents an adaptive neural network based Fuzzy Inference System (ANFIS) to reduce the false positive (FP)
rate of detected colonic polyps in Computed Tomography Colonography (CTC) scans. Extracted fuzzy rules establish
linguistically interpretable relationships in the data that are easy to understand, validate, and extend. The system takes
several features identified from regions extracted by a segmentation algorithm and decides whether the regions are true
polyps. In the training phase, subtractive clustering is used to down-sample the negative regions in order to get balanced
data. The rule extraction method is based on estimating clusters in the data using the subtractive clustering algorithm;
each cluster obtained corresponds to a fuzzy rule that maps a region in the input space to an output class. After the
number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized using a
hybrid learning algorithm which is a combination of least-squares estimation with back propagation. The evolved
Sugeno-type FIS has been tested on a total of 129 scans with 99 polyps of sizes 5-15 mm by experienced radiologists.
The results indicate that for 93% detection sensitivity (on polyps), the evolved FIS method is able to remove 88% of FPs
generated by the segmentation algorithm leaving 7.5 FP per scan. The high sensitivity rate of our results show the
promise of neuro-fuzzy classifiers as an aid for interpreting CTC examinations.
Classification plays an important role in the reduction of false positives in many computer aided detection and
diagnosis methods. The difficulty of classifying polyps lies in the variation of possible polyp shapes and sizes and
the imbalance between the number of polyp and non-polyp regions available in the training data. CAD schemes
for medical applications demand high levels of sensitivity even at the expense of keeping a certain number of
false positives. In this paper, we investigate some state-of-the-art solutions to the imbalanced data problem:
Synthetic Minority Over-sampling Technique (SMOTE) and weighted Support Vector Machines (SVM). We
tested these methods using a diverse database of CT colonography, which included a wide spectrum of dificult
cases to detect polyps. We performed several experiments with different combinations of over-sampling techniques
on training data. The results demonstrated that SVMs have achieved much better performance over C4.5 with
different over-sampling techniques. Also, the results show that weighted SVM without over-sampling can achieve
comparable performance in terms of sensitivity and specificity to conventional SVM combined with the over-sampling
approach.
Selecting a set of relevant features is a crucial step in the process of building robust classifiers. Searching all
possible subsets of features is computationally impractical for large number of features. Generally, classifiers are
used for the evaluation of the separability of a certain feature subset. The performance of these classifiers depends
on some predefined parameters. However, the choice of these parameters for a given classifier is influenced by
the given feature subset and vice versa. The computational cost for feature selection would be largely increased
by including the selection of optimal parameters for the classifier (for each subset). This paper attempts to
tackle the problem by introducing genetic algorithms (GAs) to combine the processes. The proposed approach
can choose the most relevant features from a feature set whilst simultaneously optimising the parameters of the
classifier. Its performance was tested on a colon polyp database from a cohort study using a weighted support
vector machine (SVM) classifier. As a general approach, other classifiers such as artificial neural networks (ANN)
and decision trees could be used. This approach could also be applied to other classification problems such as
other computer aided detection/diagnosis applications.
This paper deploys a wavelet based scale-space approach to extract the boundary of the object of interest in medical CT images. The classical approach of the active contour models consists of starting with an initial contour, to deform it under the action of some forces attracting the contour towards the edges by means of a set of forces. The mathematical model involves in the minimisation of an objective function called energy functional, which depends on the geometry of the contour as well as of the image characteristics. Various strategies could be used for the formulation of the energy functional and its optimisation. In this study, a wavelet based scale-space approach has been adopted. The coarsest scale is able to enlarge the capture region surrounding an object and avoids the trapping of contour into weak edges. The finer scales are used to refine the contour as close as possible to the boundary of the object. An adaptive scale coefficient for the balloon energy has been introduced. Four levels of resolution have been applied in order to get reproducibility of the contour despite poor different initialisations. The scheme has been applied to segment the regions of interest in CT lung and colon images. The result has been shown to be accurate and reproducible for the cases containing fat, holes and other small high intensity objects inside lung nodules as well as colon polyps.
Computer assisted methods for the detection of pulmonary nodules have become more important as the resolution of CT scanners has increased and as more accurate and reproducible detections are needed. In this paper we describe the results of a CAD system for the detection of lung nodules and compare them against the interpretations of three independent radiologists.
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