Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing. We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the “small sample size” problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.
This paper improves the Lateralization (identification of the epileptogenic hippocampus) accuracy in Mesial Temporal
Lobe Epilepsy (mTLE). In patients with this kind of epilepsy, usually one of the brain's hippocampi is the focus of the
epileptic seizures, and resection of the seizure focus is the ultimate treatment to control or reduce the seizures. Moreover,
the epileptogenic hippocampus is prone to shrinkage and deformation; therefore, shape analysis of the hippocampus is
advantageous in the preoperative assessment for the Lateralization. The method utilized for shape analysis is the
Spherical Harmonics (SPHARM). In this method, the shape of interest is decomposed using a set of bases functions and
the obtained coefficients of expansion are the features describing the shape. To perform shape comparison and analysis,
some pre- and post-processing steps such as "alignment of different subjects' hippocampi" and the "reduction of feature-space
dimension" are required. To this end, first order ellipsoid is used for alignment. For dimension reduction, we
propose to keep only the SPHARM coefficients with maximum conformity to the hippocampus shape. Then, using these
coefficients of normal and epileptic subjects along with 3D invariants, specific lateralization indices are proposed.
Consequently, the 1536 SPHARM coefficients of each subject are summarized into 3 indices, where for each index the
negative (positive) value shows that the left (right) hippocampus is deformed (diseased). Employing these indices, the
best achieved lateralization accuracy for clustering and classification algorithms are 85% and 92%, respectively. This is
a significant improvement compared to the conventional volumetric method.
This paper presents a new algorithm for Gleason grading of pathological images of prostate. Structural features of the glands are extracted and used in a tree-structured (TS) algorithm to classify the images into five Gleason grades of 1 to 5. In this algorithm the image is first segmented to locate the glandular regions using texture features and a K-means clustering algorithm. The glands are then labeled from the glandular regions. In each stage of the proposed TS algorithm, shape and intensity-based features of the glands are extracted and used in a linear classifier to classify the image into two groups. Despite some proposed methods in the literature which use only texture features, this technique uses the features like roundness and shape distribution, which are related to the structure of the glands in each grade and are independent of the magnification. The proposed method is therefore robust to illumination and magnification variations. To evaluate the performance of the proposed method, we use two datasets. Data set 1 contains 91 images with similar magnifications and illuminations. Data set 2 contains 199 images with different magnifications and illuminations. Using leave-one-out technique, we achieve 95% and 85% accuracy for dataset 1 and 2, respectively.
This paper presents a study of the texture information of high-resolution FLAIR images of the brain with the aim of determining the abnormality and consequently the candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. Intensity and volume features of the hippocampus from FLAIR images of the brain have been previously shown to be useful in detecting the abnormal hippocampus in TLE. However, the small size of the hippocampus may limit the texture information. High-resolution FLAIR images show more details of the abnormal intensity variations of the hippocampi and therefore are more suitable for texture analysis. We study and compare the low and high-resolution FLAIR images of six epileptic patients. The hippocampi are segmented manually by an expert from T1-weighted MR images. Then the segmented regions are mapped on the corresponding FLAIR images for texture analysis. The 2-D wavelet transforms of the hippocampi are employed for feature extraction. We compare the ability of the texture features from regular and high-resolution FLAIR images to distinguish normal and abnormal hippocampi. Intracranial EEG results as well as surgery outcome are used as gold standard. The results show that the intensity variations of the hippocampus are related to the abnormalities in the TLE.
This paper presents a study on the SPECT images of the brain with the aim of determining the hippocampus abnormality and consequently applying timely treatment. Intensity and volume features of the hippocampus from brain MRI have been shown to be useful in detecting the abnormal hippocampus in TLE. In this study, we evaluate the intensity information of the SPECT images of the brain for the purpose of early detection of abnormal hippocampus, before the brain tissue is damaged and MRI features change. The hippocampi are segmented manually by an expert from T1-weighted MR images. The segmented regions are mapped on the corresponding SPECT images using the mutual information technique. The mean and standard deviation of the hippocampi from SPECT images are used to determine abnormal hippocampus. The experimental results show that SPECT images analyzed along with MRI generate quantitative information useful for the treatment and evaluation of epileptic patients.
Intensity and volume features of the hippocampus from MR images of the brain are known to be useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy surgery. However, currently, intracranial EEG exams are required to determine the abnormal hippocampus. These exams are lengthy, painful and costly. The aim of this study is to evaluate texture characteristics of the hippocampi from MR images to help physicians determine the candidate hippocampus for surgery. We studied the MR images of 20 epileptic patients. Intracranial EEG results as well as surgery outcome were used as gold standard. The hippocampi were manually segmented by an expert from T1-weighted MR images. Then the segmented regions were mapped on the corresponding FLAIR images for texture analysis. We calculate the average energy features from 2D wavelet transform of each slice of hippocampus as well as the energy features produced by 3D wavelet transform of the whole hippocampus volume. The 2D wavelet transform is calculated both from the original slices as well as from the slices perpendicular to the principal axis of the hippocampus. In order to calculate the 3D wavelet transform we first rotate each hippocampus to fit it in a rectangular prism and then fill the empty area by extrapolating the intensity values. We combine the resulting features with volume feature and compare their ability to distinguish between normal and abnormal hippocampi using linear classifier and fuzzy c-means clustering algorithm. Experimental results show that the texture features can correctly classify the hippocampi.
This paper presents our recent study to evaluate how effectively the image texture information within the hippocampus structure can help the physicians to determine the candidates for epilepsy surgery. First we segment the hippocampus from T1-weighted images using our newly developed knowledge-based segmentation method. To extract the texture features we use multiwavelet, wavelet, and wavelet packet transforms. We calculate the energy and entropy features on each sub-band obtained by the wavelet decomposition. These texture features can be used by themselves or along with other features such as shape and average intensity to classify the hippocampi. The features are calculated on the T1-weighted and FLAIR MR images. Using these features, a clustering algorithm is applied to classify each hippocampus. To find the optimal basis, we use several different bases for wavelet and multiwavelet transforms, and compare the final classification performances, which is evaluated by correct classification rate (CCR). We use MRI of 14 epileptic patients along with their EEG results in our study. We use the pre-operative MR images of the patients who have already been determined as candidates for an epilepsy surgery using the gold standard (more costly and painful) methods of EEG phase II study. Experimental results show that the texture features may predict the candidacy for epilepsy surgery. If successful in large population studies, the proposed non-invasive method can replace invasive and costly EEG studies.
We have developed image analysis methods to automatically grade pathological images of prostate. The proposed method generates Gleason grades to images, where each image is assigned a grade between 1 and 5. This is done using features extracted from multiwavelet transformations. We extract energy and entropy features from submatrices obtained in the decomposition. Next, we apply a k-NN classifier to grade the image. To find optimal multiwavelet basis, preprocessing, and classifier, we use features extracted by different multiwavelets with either critically sampled preprocessing or repeated row preprocessing and different k-NN classifiers and compare their performances, evaluated by total misclassification rate (TMR). To evaluate sensitivity to noise, we add white Gaussian noise to images and compare the results (TMR's). We applied proposed methods to 100 images. We evaluated the first and second levels of decomposition using Geronimo, Hardin, and Massopust (GHM), Chui and Lian (CL), and Shen (SA4) multiwavelets. We also evaluated k-NN classifier for k=1,2,3,4,5. Experimental results illustrate that first level of decomposition is quite noisy. They also show that critically sampled preprocessing outperforms repeated row preprocessing and has less sensitivity to noise. Finally, comparison studies indicate that SA4 multiwavelet and k-NN classifier (k=1) generates optimal results (with smallest TMR of 3%).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.