Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.

Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm

Image segmentation of brain magnetic resonance imaging (MRI) plays a crucial role among radiologists in terms of diagnosing brain disease. Parts of the brain such as white matter, gray matter and cerebrospinal fluids (CFS), have to be clearly determined by the radiologist during the process of brain abnormalities detection. Manual segmentation is grueling and may be prone to error, which can in turn affect the result of the diagnosis. Nature-inspired metaheuristic algorithms such as Harmony Search (HS), which was successfully applied in multilevel thresholding for brain tumor segmentation instead of the Patch-Levy Bees algorithm (PLBA). Even though the PLBA is one powerful multilevel thresholding, it has not been applied to brain tumor segmentation. This paper focuses on a comparative study of the PLBA and HS for brain tumor segmentation. The test dataset consisting of nine images was collected from the Tuanku Muhriz UKM Hospital (HCTM). As for the result, it shows that the PLBA has significantly outperformed HS. The performance of both algorithms is evaluated in terms of solution quality and stability

Round Randomized Learning Vector Quantization for Brain Tumor Imaging

Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system

Spinal Schwannomatosis Mimicking Metastatic Extramedullary Spinal Tumor

Intradural extramedullary (IDEM) tumors are the most commonly observed intraspinal tumors, comprising over 60% of tumors found within the spinal canal, and the vast majority of these lesions are benign lesions. IDEM metastases are rare, but if they occur, they commonly manifest as leptomeningeal disease, secondary to drop lesions from intracranial metastases from adenocarcinomas of the lung, prostate cancer, breast cancer, melanoma, or rarely, as a result of lymphomas. The purely non-neurogenic origin of IDEM metastases is rare. Herein, we describe a patient with a previous history of treated colon cancer who presented with a progressive neurological deficit and whose imaging revealed multiple intradural, extramedullary and osseous lesions at the cervical and thoracolumbar spines. With the previous known primary and multiplicity of the lesions, an initial diagnosis of spinal metastasis was made, But it was proven to be schwannoma on histology. We emphasize the diagnostic dilemma in this case and the importance of detecting subtle imaging findings, which may be helpful to differentiate between metastatic disease and a second primary tumor

Encapsulated Papillary Carcinoma: A Rare Case Report and Its Imaging Features

Papillary lesions in the breasts are uncommon and have a wide range of pathologies. Due to diverse non-specific findings radiologically and histologically, papillary neoplasms are always a challenge to radiologists. Encapsulated papillary carcinomas (EPCs) of the breast, also known as intracystic papillary carcinomas, are a subgroup of intraductal papillary lesions of the breast. We present a case of a painless right breast lump with the aim to describe a rare encapsulated papillary carcinoma and its imaging features

A Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and T2-Weighted Imaging in Determining the Depth of Myometrial Invasion in Endometrial Carcinoma—A Retrospective Study

This study aims to compare dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with T2-weighted imaging (T2WI) in defining the depth of myometrial invasion in endometrial carcinoma. This retrospective study included 32 subjects with endometrial carcinoma who underwent 3.0T magnetic resonance imaging (MRI) prior to hysterectomy. DCE-MRI and T2WI were evaluated to determine the depth of myometrial invasion in endometrial carcinoma. A set of data consisting of the sensitivity, specificity, predictive values, and accuracy of DCE-MRI and T2WI were obtained and compared with the histopathological results. Out of the 32 cases included, the histopathological examination revealed that 50% myometrial invasion was found in 11 patients and ≥50% myometrial invasion was found in 21 patients. In the assessment of the tumor invasion, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of T2WI were 45.45%, 90.48 %, 71.43%, 76.0%, and 75.0%, respectively. The corresponding values for DCE-MRI were 81.82%, 76.19%, 64.29%, 88.89 %, and 78.12%, respectively. When T2WI were read together with DCE-MRI, the values were 90.91%, 90.48%, 83.33%, 95.0%, and 90.62%, respectively. Thus, the sensitivity and accuracy of DCE-MRI were greater compared to T2WI in defining the depth of myometrial invasion. However, the merging of T2WI and DCE-MRI increased the specificity and PPV value and improved the sensitivity, NPV and accuracy in detecting myometrial invasion. DCE-MRI was more sensitive but less specific than T2WI in defining the depth of myometrial invasion. In conclusion, combining DCE-MRI and T2WI further improves the diagnostic performance for myometrial invasion in endometrial carcinoma

A Rare Case of Post-Traumatic Cervical Ligamentous Tear Complicated by Vertebral Arteriovenous Fistula (vAVF), with Successful Endovascular Treatment

Post-traumatic vertebral arteriovenous fistula (vAVF) caused by motor vehicle accidents (MVA) is a rare condition in which there is abnormal communication between the vertebral artery and its adjacent veins. In a post-MVA setting, it is commonly associated with vertebral body fracture. In this paper, we report a case of a 19-year-old girl with a complete C2/C3 anterior and posterior ligament tear post MVA without any cervical bony injury. Initial plain computed tomography (CT) cervical scan showed a prevertebral hematoma. A CT angiogram (CTA) raised the suspicion of a pseudo-aneurysm at the right posterior C3 vertebral body. Further imaging with magnetic resonance imaging (MRI) demonstrated traumatic AVF at the C2/C3 level involving the V2/V3 right vertebral artery to the vertebral venous plexus. Digital Subtraction Angiography (DSA) further revealed a transected right vertebral artery at the C2/C3 level with an arteriovenous fistula and an enlarged vertebral venous plexus. The fistulous communication was successfully occluded with coils from a cranial and caudal approach to the transected segment right vertebral artery, with a total of eight coils. Post-MVA vertebral arteriovenous fistula (vAVF) is a rare sequela of vertebral bony injury at the cervical region, and is an even rarer association with an isolated ligamentous injury, whereby endovascular treatment with ipsilateral vertebral artery closure is a feasible treatment of vAVF

Comparison of Mean Glandular Dose between Full-Field Digital Mammography and Digital Breast Tomosynthesis

Early detection of breast cancer is diagnosed using mammography, the gold standard in breast screening. However, its increased use also provokes radiation-induced breast malignancy. Thus, monitoring and regulating the mean glandular dose (MGD) is essential. The purpose of this study was to determine MGD for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) in the radiology department of a single centre. We also analysed the exposure factors as a function of breast thickness. A total of 436 patients underwent both FFDM and DBT. MGD was auto calculated by the mammographic machine for each projection. Patients’ data included compressed breast thickness (CBT), peak kilovoltage (kVp), milliampere-seconds (mAs) and MGD (mGy). Result analysis showed that there is a significant difference in MGD between the two systems, namely FFDM and DBT. However, the MGD values in our centre were comparable to other centres, as well as the European guideline (<2.5 mGy) for a standard breast. Although DBT improves the clinical outcome and quality of diagnosis, the risk of radiation-induced carcinogenesis should not be neglected. Regular quality control testing on mammography equipment must be performed for dose monitoring in women following a screening mammography in the future