5 research outputs found
Detection of prostate cancer using multi-parametric magnetic resonance
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (leaves 26-28).A multi-channel statistical classifier to detect prostate cancer was developed by combining information from 3 different MR methodologies: T2-weighted, T2-mapping, and Line Scan Diffusion lmaging(LSDI). From these MR sequences, 4 sets of image intensities were obtained: T2-weighted(T2W) from T2-weighted imaging, Apparent Diffusion Coefficient(ADC) from LSDI, and Proton Density (PD) and T2 (T2Map) from T2-mapping imaging. Manually- segmented tumor labels from a radiologist were validated by biopsy results to serve as tumor "ground truth." Textural features were derived from the images using co-occurrence matrix and discrete cosine transform. Anatomical location of voxels was described by a cylindrical coordinate system. Statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood(ML) classifiers were based on 1 of the 4 basic image intensities. Our multi-channel classifiers: support vector machine (SVM) and fisher linear discriminant(FLD), utilized 5 different sets of derived features. Each classifer generated a summary statistical map that indicated tumor likelihood in the peripheral zone(PZ) of the gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves were compared. Our best FLD classifier achieved an average ROC area of 0.839 (±0.064) and our best SVM classifier achieved an average ROC area of 0.761 (±0.043). The T2W intensity maximum likelihood classifier, our best single-channel classifier, only achieved an average ROC area of 0.599 (± 0.146). Compared to the best single-channel ML classifier, our best multi-channel FLD and SVM classifiers have statistically superior ROC performance with P-values of 0.0003 and 0.0017 respectively from pairwise 2-sided t-test. By integrating information from the multiple images and capturing the textural and anatomical features in tumor areas, the statistical summary maps can potentially improve the accuracy of image-guided prostate biopsy and enable the delivery of localized therapy under image guidance.by Ian Chan.M.Eng
Analisi sperimentali di modelli neurali per la classificazione in immagini di risonanza magnetica del seno.
Lo scopo di questo studio è sperimentare diverse reti neurali come strumento di supporto ai medici nella diagnosi del cancro al seno.
I dati utilizzati sono parametri dinamici estratti da immagini di risonanza magnetica del seno combinati in diversi modi per ottenere
la migliore classificazione delle lesioni
Automatic BIRAD scoring of breast cancer mammograms
A computer aided diagnosis system (CAD) is developed to fully characterize and
classify mass to benign and malignancy and to predict BIRAD (Breast Imaging
Reporting and Data system) scores using mammographic image data. The CAD
includes a preprocessing step to de-noise mammograms. This is followed by an
active counter segmentation to deforms an initial curve, annotated by a
radiologist, to separate and define the boundary of a mass from background. A
feature extraction scheme wasthen used to fully characterize a mass by extraction
of the most relevant features that have a large impact on the outcome of a patient
biopsy. For this thirty-five medical and mathematical features based on intensity,
shape and texture associated to the mass were extracted. Several feature selection
schemes were then applied to select the most dominant features for use in next
step, classification. Finally, a hierarchical classification schemes were applied on
those subset of features to firstly classify mass to benign (mass with BIRAD score
2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify
mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c).
Accuracy of segmentation performance were evaluated by calculating the degree
of overlapping between the active counter segmentation and the manual
segmentation, and the result was 98.5%. Also reproducibility of active counter
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using different manual initialization of algorithm by three radiologists were
assessed and result was 99.5%.
Classification performance was evaluated using one hundred sixty masses (80
masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result
for classification of data to benign and malignance was found using a combination
of sequential forward floating feature (SFFS) selection and a boosted tree hybrid
classifier with Ada boost ensemble method, decision tree learner type and 100
learners’ regression tree classifier, achieving 100% sensitivity and specificity in
hold out method, 99.4% in cross validation method and 98.62 % average accuracy
in cross validation method.
For further sub classification of eighty malignance data with BIRAD score of over
4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with
BIRAD score 4c), the best result achieved using the boosted tree with ensemble
method bag, decision tree learner type with 200 learners Classification, achieving
100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41%
average accuracy for ten times run in cross validation method.
Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score
3 were gathered. Which means patient is recommended to be tested in another
medical imaging technique and also is recommended to do follow-up in six
months. The CAD system was trained with mass with BIRAD score 2 and over 4 also
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it was further tested using 13 masses with a BIRAD score of 3 and the CAD results
are shown to agree with the radiologist’s classification after confirming in six
months follow up.
The present results demonstrate high sensitivity and specificity of the proposed
CAD system compared to prior research. The present research is therefore
intended to make contributions to the field by proposing a novel CAD system,
consists of series of well-selected image processing algorithms, to firstly classify
mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and
finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study
Recommended from our members
Digital Image Processing via Combination of Low-Level and High-Level Approaches.
With the growth of computer power, Digital Image Processing plays a more
and more important role in the modern world, including the field of industry,
medical, communications, spaceflight technology etc. There is no clear
definition how to divide the digital image processing, but normally, digital
image processing includes three main steps: low-level, mid-level and highlevel
processing.
Low-level processing involves primitive operations, such as: image preprocessing
to reduce the noise, contrast enhancement, and image sharpening.
Mid-level processing on images involves tasks such as segmentation (partitioning
an image into regions or objects), description of those objects to
reduce them to a form suitable for computer processing, and classification
(recognition) of individual objects. Finally, higher-level processing involves
"making sense" of an ensemble of recognised objects, as in image analysis.
Based on the theory just described in the last paragraph, this thesis is
organised in three parts: Colour Edge and Face Detection; Hand motion
detection; Hand Gesture Detection and Medical Image Processing.
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In Colour Edge Detection, two new images G-image and R-image are
built through colour space transform, after that, the two edges extracted
from G-image and R-image respectively are combined to obtain the final
new edge. In Face Detection, a skin model is built first, then the boundary
condition of this skin model can be extracted to cover almost all of the skin
pixels. After skin detection, the knowledge about size, size ratio, locations
of ears and mouth is used to recognise the face in the skin regions.
In Hand Motion Detection, frame differe is compared with an automatically
chosen threshold in order to identify the moving object. For some special
situations, with slow or smooth object motion, the background modelling
and frame differencing are combined in order to improve the performance.
In Hand Gesture Recognition, 3 features of every testing image are input
to Gaussian Mixture Model (GMM), and then the Expectation Maximization
algorithm (EM)is used to compare the GMM from testing images and GMM
from training images in order to classify the results.
In Medical Image Processing (mammograms), the Artificial Neural Network
(ANN) and clustering rule are applied to choose the feature. Two
classifier, ANN and Support Vector Machine (SVM), have been applied to
classify the results, in this processing, the balance learning theory and optimized
decision has been developed are applied to improve the performance