الفهرس 
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Abstract
Early detection and accurate staging of a cancer is considered an important issue
in practical radiology. It can increase the possibility of human survive. Medical imaging
is the process of human body visualization. It plays an essential role in human body
diagnosis and treatment. Much information can be obtained from di erent medical
imaging techniques like CT and MRI, which it needs an expert to evaluate and analysis
these types of images in short time. Also, it is quite common that for same set of medical
imaging, di erent doctors may come up with di erent diagnosis results. Computer-Aided
Diagnosis (CAD) has become one of the major research subjects in diagnostic medical
imaging. The basic concept of CAD is to provide a fully automatic system to assist
radiologists in medical image interpretation. Moreover, it can help surgeons through
identifying the location and size of tumors.
The main objective of the thesis is to introduce a modi ed computer-aided diagnosis
(CAD) system in medical imaging, since medical images are relevant sources of information
for detecting and diagnosing a large number of illnesses and abnormalities. Due to
their importance, this thesis is focused on several medical imaging type including (1) CT
liver images, (2) chromosomes histopathology slide imaging, (3) breast histopathology
slide imaging and (4) breast thermal imaging. The proposed CAD system is mainly
divided into ve sub CAD systems. Each sub CAD system is dealing with di erent
medical imaging modalities such as CT, Thermal and Histopathology. Moreover it is
divided into four parts: (1) organ region segmentation, (2) detection and extraction of
abnormalities regions, (3) features extraction for abnormalities candidates regions and
nally (4) candidate classi cation to normal or abnormal (benign or malignant). All
the proposed sub CAD systems in this thesis are mainly depend on two approaches;
Neutrosophic Sets and Swarm Optimization approaches.
In the rst investigation in this thesis, a fully automatic mitosis detection and counting
system for breast cancer histopathology slide imaging using Neutrosophic Sets has
been presented. In this system the candidates have been extracted using Neutrosophic
image. In order to enhance the extracted candidate, morphological operators have been
used which helps in eliminating too small regions that are non-mitosis. Several features
have been extracted from the detected candidates that are focused on statistical, texture
and shape features.
The second study presented a system for automatic classi cation of thermogram
imaging to normal or abnormal. This system consists of two main phases: (1) automatic
segmentation done by Neutrosophic sets in conjunction with fuzzy c-means to get region
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of interest (ROI); (2) classi cation achieved by extracting several features, i.e. statistical,
texture and energy, and then classi ed by SVM to into normal and abnormal.
Interphase cells are undivided and the condensedmass of chromosomes. They can
highly decrease the eciency of automatic karyotype. Therefore, a new fully automatic
system based on fast fuzzy c-means (FFCM) and grey wolf swarms optimization
(GWO) has been presented. The proposed system used to remove interphase cells and
extract chromosomes from metaphase chromosomes image. It comprised of three phases,
namely; preprocessing, chromosomes image clustering and post-processing phase. The
obtained results show a good performance of the proposed system.
The fourth study presented a fully automatic CAD system for CT liver image
diagnosis based on using GWO. The proposed system comprised of four phases: (1) liver
segmentation, (2) lesion segmentation, (3) features extraction from each candidate and
nally (4) candidates lesion classi cation. The experimental results show the eciency
of the proposed system. Moreover, it has been compared with other systems.
Finally, an integrated system based on using PSO approach and watershed algorithm
for automatic liver segmentation from abdominal CT images has been proposed.
Several measurements are used to evaluate the performance of the proposed systems
for di erent applications. These measurements are Correlation, Speci city, Dice
Coecient, Jacard Index, Accuracy, Sensitivity, F-measure, True Positive Ratio, Error
Rate, Precision and Recall. The experimental results show that the proposed liver segmentation
system using PSO for 43 images obtains 94% and using GWO for 62 images
obtains 96% and obtains 97% for liver diagnosis. The proposed detection and counting
system for breast cancer histopathology slide using 35 benchmark histopathological
data base images taken from ve di erent aspects obtains overall using Neutrosophic
approach 77.61% F-score, Recall 74.92% and Precision 81.25 % with accuracy 90.1%.
The proposed CAD system for breast cancer detection from thermal image using 61
breast cancer thermal bench mark dataset images using Neutrosophic approach obtains
overall 92.06% Accuracy, 96.55% Recall, 87.50% Precision and 7.94% Error rate. The
proposed segmentation and interphase cells removal system from chromosomes slide images
GWO for 30 chromosomes slide images obtains overall 93.61% Accuracy, 90.99%
Precision, 89.61% Sensitivity, 6.39% Misclassi cation Rate and 96.35% Speci city.