41,137 research outputs found
Classification of interstitial lung disease patterns with topological texture features
Topological texture features were compared in their ability to classify
morphological patterns known as 'honeycombing' that are considered indicative
for the presence of fibrotic interstitial lung diseases in high-resolution
computed tomography (HRCT) images. For 14 patients with known occurrence of
honey-combing, a stack of 70 axial, lung kernel reconstructed images were
acquired from HRCT chest exams. A set of 241 regions of interest of both
healthy and pathological (89) lung tissue were identified by an experienced
radiologist. Texture features were extracted using six properties calculated
from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and
three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN)
classifier and a Multilayer Radial Basis Functions Network (RBFN) were
optimized in a 10-fold cross-validation for each texture vector, and the
classification accuracy was calculated on independent test sets as a
quantitative measure of automated tissue characterization. A Wilcoxon
signed-rank test was used to compare two accuracy distributions and the
significance thresholds were adjusted for multiple comparisons by the
Bonferroni correction. The best classification results were obtained by the MF
features, which performed significantly better than all the standard GLCM and
MD features (p < 0.005) for both classifiers. The highest accuracy was found
for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively).
The best standard texture features were the GLCM features 'homogeneity' (91.8%,
87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced
topological texture features can provide superior classification performance in
computer-assisted diagnosis of interstitial lung diseases when compared to
standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201
Evaluation of Statistical Features for Medical Image Retrieval
In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics
of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa-
chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy
Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach
While small, fragmented wooded elements do not represent a large surface area in
agricultural landscape, their role in the sustainability of ecological processes is
recognized widely. Unfortunately, landscape ecology studies suffer from the lack
of methods for automatic detection of these elements. We propose a hybrid
approach using both aerial photographs and ancillary data of coarser resolution
to automatically discriminate small wooded elements. First, a spectral and textural
analysis is performed to identify all the planted-tree areas in the digital photograph.
Secondly, an object-orientated spatial analysis using the two data sources
and including a multi-resolution segmentation is applied to distinguish between
large and small woods, copses, hedgerows and scattered trees. The results show the
usefulness of the hybrid approach and the prospects for future ecological
applications
Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles
We investigate the usefulness of quantitative ultrasound (QUS) and B-mode
texture features for characterization of ulnar nerve fascicles. Ultrasound data
were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the
nerves were extracted to prepare histology sections. 85 fascicles were matched
between the B-mode images and the histology sections. For each fascicle image,
we selected an intra-fascicular region of interest. We used histology sections
to determine features related to the concentration of collagen and myelin, and
ultrasound data to calculate backscatter coefficient (-24.89 dB 8.31),
attenuation coefficient (0.92 db/cm-MHz 0.04), Nakagami parameter (1.01
0.18) and entropy (6.92 0.83), as well as B-mode texture features
obtained via the gray level co-occurrence matrix algorithm. Significant
Spearman's rank correlations between the combined collagen and myelin
concentrations were obtained for the backscatter coefficient (R=-0.68), entropy
(R=-0.51), and for several texture features. Our study demonstrates that QUS
may potentially provide information on structural components of nerve
fascicles
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.Comment: 20 pages, 7 figures and 4 table
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