Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary
nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor
pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature
extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not
require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one
background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we
calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector
machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the
lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results
demonstrate that the proposed strategy is promising.
In this paper, we proposed a novel method to extract shape feature based on dual-tree complex wavelet. First, with the two level dual-tree complex wavelet transformations, we can get two low frequency components of the first level, which are used as wavelet moment invariants formed from approximation coefficients. Then, we calculate means and variance for each of the six detailed components in the second level since it contains different directions information of the shape. Using the Principal Component Analysis (PCA), twenty features can be reduced to five maximum useful features which contribute to shape matching.
We are developing an automated method for detection and quantification of ischemic stroke in computed tomography (CT). Ischemic stroke often connects to brain ventricle, therefore, ventricular segmentation is an important and difficult task when stroke is present, and is the topic of this study. We first corrected inclination angle of brain by aligning midline of brain with the vertical centerline of a slice. We then estimated the intensity range of the ventricles by use of the k-means method. Two segmentation of the ventricle were obtained by use of thresholding technique. One segmentation contains ventricle and nearby stroke. The other mainly contains ventricle. Therefore, the stroke regions can be extracted and removed using image difference technique. An adaptive template-matching algorithm was employed to identify objects in the fore-mentioned segmentation. The largest connected component was identified and considered as the ventricle. We applied our method to 25 unenhanced CT scans with stroke. Our method achieved average Dice index, sensitivity, and specificity of 95.1%, 97.0%, and 99.8% for the entire ventricular regions. The experimental results demonstrated that the proposed method has great potential in detection and quantification of stroke and other neurologic diseases.
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