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.
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.
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.
Multidetector row CT, multiphase CT in particular, has been widely accepted as a sensitive imaging modality in
the detection of liver cancer. Segmentation of liver from CT images is of great importance in terms of accurate
detection of tumours, volume measurement, pre-surgical planning. The segmentation of liver, however, remains
to be an unsolved problem due to the complicated nature of liver CT such as imaging noise, similar intensity to
its adjacent structures and large variations of contrast kinetics and localised geometric features. The purpose
of this paper is to present our newly developed algorithm aiming to tackle this problem. In our method, a CT
image was first smoothed by geometric diffusion method; the smoothed image was segmented by thresholding
operators. In order to gain optimal segmentation, a novel method was developed to choose threshold values
based on both the anatomical knowledge and features of liver CT. Then morphological operators were applied
to fill the holes in the generated binary image and to disconnect the liver from other unwanted adjoining
structures. After this process, a so-called "2.5D region overlapping" filter was introduced to further remove
unwanted regions. The resulting 3D region was regarded as the final segmentation of the liver region. This
method was applied to venous phase CT data of 45 subjects (30 patient and 15 asymptomatic subjects). Our
results show good agreement with the annotations delineated manually by radiologists and the overlapping ratio
of volume is 87.7% on average and the correlation coefficient between them is 98.1%.
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