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Fat extraction is a crucial aspect of diagnostic analysis in T1-weighted magnetic resonance imaging (MRI) images. However, the accuracy is affected by image inhomogeneity. Inhomogeneity refers to variations in signal intensity across an image, which can be caused by uneven magnetic fields or abnormal fluids in MRI image. This study uses fuzzy C-means (FCM) algorithm for fat region extraction. However, FCM is struggle with regions of similar intensity. The objective of this study is to propose a method for inhomogeneity correction using adaptive disk structure element morphological (ADSEM) approach. This rectifies the impact of inhomogeneity-induced intensity variations. The method is then integrated with FCM for fat extraction. This approach overcome FCM's intensity similarity limitation, enhancing fat extraction accuracy. Comparative assessments highlight the integrated ADSEM-FCM method's superiority over FCM. The quantitative assessment for proposed method in term of accuracy, recall, precision and F1 score is 0.9246, 0.9777, 0.7740, and 0.8526 respectively. [ABSTRACT FROM AUTHOR]

Abstract: We proposed an expert system based on the interpretation of mammographic and ultrasound images that may be used by expert and non-expert doctors in the interpretation and classifying of patient cases. The expert system software consists of a mammographic(MAMMEX) and breast ultrasound(SOUNDEX) medical expert systems which may be used to deduce cases according to the Breast Imaging Recording and Data System (BI-RADS) based upon patients' history, physical and clinical assessment as well as mammograms and breast ultrasound images. The systems were tested on a total of 179 retrospective cases from the Radiology Department, Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian, Kelantan. The accuracy, sensitivity and specificity of MAMMEX were 97%, 96% and 92% and values of 99%, 98% and 100% were found for SOUNDEX respectively. Key Words: Expert Systems, Knowledge-Based Systems, Mammograph, Ultrasound, Breast Cancer, INTRODUCTION Breast cancer is the most common form of malignant disease amongst women. However, mortality rates fell noticeably especially in the United Kingdom partly because of the widespread practice of [...]

Background: The width of frontal sinus outflow tract is determined by pneumatisation pattern of frontal recess cell. The purpose of this study is to determine correlation between volume of agger Nasi cell (ANC) with frontal beak thickness, A-P length of frontal isthmus (FI) and frontal recess (FR). Materials and Methods: A cross sectional study was conducted in 140 adult patients who had computed tomography (CT) brain done in HUSM from Jan 2010 to June 2018. Multiplanar reconstructed images then retrieved from PACS system. Prevalence and pattern of pneumatisation of frontal recess cell reviewed. Agger Nasi cell volume measured with analysis of its correlation to frontal beak thickness, length of frontal recess and frontal isthmus done by researcher using Pearson correlation test. Result: ANC found in 131 out of 140 adult patients (93.6%). Mean ANC volume 434.48 with standard deviation of 256.63. For frontal beak (FB) thickness, anterior-posterior (A-P) length of FI & FR, mean values were 6.24 (SD 1.61), 7.06 (SD 2.92) and 4.95 (SD 2.97) respectively. No significant correlation was found between volume of ANC with FB thickness, A-P length of FI and FR. Conclusion: There was no statistically significant correlation between volume of ANC with the frontal beak thickness, anterior posterior length of frontal isthmus and frontal recess (p>0.05). Since ANC is commonly found in population, it is still essential for radiologist to consider reporting the ANC volume and pattern of pneumatisation when endoscopic surgery is planned. [ABSTRACT FROM AUTHOR]