A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria.

Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.

Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling.

BACKGROUND: Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. OBJECTIVES: In this study, a Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. METHODS: The method presented incorporates information fusion from multiple modalities, modelling of image resolution (measurement blurring), image noise, two priors helping to reduce noise and partial volume. Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation. Regularization based on isotropic and directional Markov Random Field priors is integrated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image. Sub-methods as variations of the main method are generated by using a combination of the models. RESULTS: Testing of the sub-methods is performed on two patients using single modality three-dimensional (3D) image (magnetic resonance, MR or computerized tomography, CT) as well as registered MR-CT images with information fusion. Numerical, visual and statistical analyses of the methods are conducted. High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images. The methods are also compared with our Bayesian segmentation method proposed in a previous study. The performance is found to be similar to our previous Bayesian approach, but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent. CONCLUSIONS: The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head. Meanwhile, free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more systematic way as compared with our previous study.

Image analysis for cystic fibrosis: computer-assisted airway wall and vessel measurements from low-dose, limited scan lung CT images.

Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results.

Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study.

The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method.

Computerized scar detection on renal cortical scintigraphy images.

OBJECTIVE: Renal cortical scintigraphy is a well-established functional imaging technique for visual analysis of radiopharmaceutical tracer distribution. However, the visual evaluation is subjective, causing interobserver variability, especially in a quantifiable number of scars. The purpose of this study was to develop new computerized methods in renal cortical scintigraphy image interpretation, particularly addressing activity distribution and cortex continuity (scars). METHODS: The proposed methods involve preprocessing stages of model-based automatic kidney segmentation using active-shape model and image normalization (transforming each kidney image into a standardized image vector). For our previous computer-aided diagnosis scheme, two new image-based features [localized activity drop and principal component analysis (PCA)] were defined. Their performance was evaluated and compared with our previous scheme by using free-response receiver operating characteristic that is in terms of sensitivity (true-positive fraction) and the mean number of false positives (FPs) per image. RESULTS: Clinical tests were conducted in 297 patients (231 normal and 66 abnormal). The PCA-based image feature presented the best scar detection performance, followed by the localized activity drop feature. Both schemes were found to be superior to our previous computer-aided diagnosis scheme. In the PCA-based scheme, for sensitivity of 0.90 (76/84), the mean number of FPs was measured as 4.52 (1343/297). For another setting with reduced sensitivity of 0.79 (66/84), the mean number of FPs improved to 1.21 (360/297). Finally, a decision fusion scheme using 'majority voting' was also proposed, the sensitivity and mean number of FPs of which were measured as 0.83 (70/84) and 1.90 (563/297), respectively. CONCLUSION: The proposed methods have potential to provide effective second-reader information to nuclear medicine specialists in finding scar regions. Possible ways to improve the FP rate were also proposed.

Image analysis for cystic fibrosis: automatic lung airway wall and vessel measurement on CT images.

Cystic Fibrosis (CF) is the most common lethal genetic disorder in the Caucasian population, affecting about 30,000 people in the United States. It results in inflammation, hence thickening of airway (AW) walls. It has been demonstrated that AW inflammation begins early in life producing structural AW damage. Because this damage can be present in patients who are relatively asymptomatic, lung disease can progress insidiously. High-resolution computed tomographic imaging has also shown that the AWs of infants and young children with CF have thicker walls and are more dilated than those of normal children. The purpose of this study was to develop computerized methods which allow rapid, efficient and accurate assessment of computed tomographic AW and vessel (V) dimensions from axial CT lung images. For this purpose, a full-width-half-max based automatic AW and V size measurement method was developed. The only user input required is approximate center marking of AW and V by an expert. The method was evaluated on a patient population of 4 infants and 4 children with different stages of mild CF related lung disease. This new automated method for assessing early AW disease in infants and children with CF represents a potentially useful outcome measure for future intervention trials.

A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets.

In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification.

SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees.

A new method based on probabilistic suffix trees (PSTs) is defined for pairwise comparison of distantly related protein sequences. The new definition is adopted in a discriminative framework for protein classification using pairwise sequence similarity scores in feature encoding. The framework uses support vector machines (SVMs) to separate structurally similar and dissimilar examples. The new discriminative system, which we call as SVM-PST, has been tested for SCOP family classification task, and compared with existing discriminative methods SVM-BLAST and SVM-Pairwise, which use BLAST similarity scores and dynamic-programming-based alignment scores, respectively. Results have shown that SVM-PST is more accurate than SVM-BLAST and competitive with SVM-Pairwise. In terms of computational efficiency, PST-based comparison is much better than dynamic-programming-based alignment. We also compared our results with the original family-based PST approach from which we were inspired. The present method provides a significantly better solution for protein classification in comparison with the family-based PST model.

Simultaneous surface registration of ictal and interictal SPECT and magnetic resonance images for epilepsy studies.

BACKGROUND: Subtraction of ictal and interictal single photon emission computed tomography (SPECT) images is known to be successful in localizing the seizure focus in the pre-surgical evaluation of patients with partial epilepsy. A computer-aided methods for producing subtraction ictal SPECT co-registered to the magnetic resonance image (MRI) (the SISCOM method) is commonly used. The two registrations involved in SISCOM are (1) between the ictal-interictal SPECT images, which was shown to be the more critical, and (2) between the ictal image and MRI. OBJECTIVE: To improve the accuracy of ictal-interictal registration in SISCOM by registering all three images (ictal, interictal SPECT, MRI) simultaneously. METHODS: The registration problem is formulated as the minimization of a cost function between three surfaces. Then, to achieve a global minimum of this cost function, the Powell algorithm with randomly distributed initial configurations is used. This technique is tested by a realistic simulation study, a phantom study and a patient study. RESULTS: The results of the simulation study demonstrate that, in surface-based registration, the triple-registration method results in a smaller ictal-interictal SPECT registration error than the pair-wise registration method (P<0.05) for a range of values of the cost-function parameter. However, the improved registration error is still larger than that obtained by the normalized mutual information method (P<0.001), which is a voxel-based registration algorithm. The phantom and patient studies reveal no observable difference between registration results. CONCLUSIONS: Although the improved accuracy of triple registration is slightly worse than voxel-based registration, it will soon be possible to apply the results of this study in research utilizing the triple-registration principle to improving voxel-based results of ictal-interictal registration.

Sampling of sentinel lymph nodes: a simulation study.

OBJECTIVE: To analyze the effects of various slicing schemes on the detection of metastases in lymph nodes. STUDY DESIGN: Use of an advanced computer simulation tool. RESULTS: Bisectioning along the longitudinal axis is an inadequate approach. Slicing ellipsoid lymph nodes along their longitudinal axis also results in a lower rate of detecting metastases since metastatic deposits have a predilection to localize subcapsularly. CONCLUSION: Ellipsoid lymph nodes must be sliced perpendicular to the longest axis to increase the rate of detecting metastases.