Perbandingan Dos Sinaran antara Prosedur Urografi Intravena (IVU) dan Tomografi Berkomputer Helikal Tanpa Kontras (UHCT) Urografi

Urografi intravena (IVU) dan tomografi berkomputer helikal tanpa kontras (UHCT) urografi adalah dua prosedur utama yang akan dijalankan semasa penyiasatan radiologi bagi pengesanan urolitiasis (batu karang) pada sistem genitourinari. Dedahan terhadap sinaran radiasi merupakan faktor kebimbangan utama dalam kedua-dua prosedur. Oleh itu, satu kajian perbandingan dos sinaran telah dijalankan antara prosedur IVU dan UHCT urografi di samping menentukan faktor dedahan optimum bagi kedua-dua prosedur tersebut. Kajian ini telah dijalankan ke atas fantom antropomorfi k seluruh tubuh mengikut protokol sebenar bagi prosedur UHCT urografi dan penghasilan radiografi bersiri berserta dengan pemberian media berkontras bagi prosedur IVU. Sebanyak tiga parameter dedahan voltan tiub digunakan iaitu 75 kVp, 80 kVp dan 85 kVp bagi prosedur IVU dan 100 kVp, 120 kVp dan 140 kVp bagi prosedur UHCT urografi . Hasil dos sinaran bagi prosedur IVU yang diperolehi adalah 1.40 mSv, 2.10 mSv dan 2.79 mSv bagi 75 kVp, 80 kVp dan 85 kVp. Manakala bagi prosedur UHCT urografi , sebanyak 0.76 mSv, 1.32 mSv dan 1.82 mSv dos sinaran direkodkan bagi 100 kVp, 120 kVp dan 140 kVp. Hasil kualiti imej optimum adalah menggunakan dedahan sebanyak 85 kVp bagi prosedur IVU dan 120 kVp bagi prosedur UHCT urografi . Kesimpulannya, walaupun tidak terdapat perbezaan signifi kan, dos sinaran yang terhasil daripada prosedur IVU adalah kekal lebih tinggi daripada prosedur UHCT urografi

Multiclass classification for chest x-ray images based on lesion location in lung zones

Innovation in radiology technology has generated numerous kinds of medical images like the chest X-ray (CXR).This image is used to find common problem in lung like the lesion through scanning process in lung area which is divided into six zones.By classifying the CXR images with common feature like the lesion location, we can ensure efficient image retrieval.Recently, Support Vector Machine (SVM) has turn out to be a well-known method for image classification.While many previous studies have reported the achievement of SVM in classifying images, yet there is still problem with this technique for multiclass classification.Since SVM is a binary classification technique, its ability is limited to classifying features between two classes at one time. Therefore, it is difficult to classify CXR images which contain many image features.Realizing the problem, we proposed an application method for multiclass classification with SVM to the CXR images based on the lesion position in the lung zones.The multiclass classification application is executed on the CXR images taken from Japan Society of Radiology Technology dataset.Lesion coordinates were selected as the classification input while the lung zones becomes the labels. The multiclass classification is performed with RBF kernel and the classification accuracy is tested to attain the classifiers performance.Overall, it can be concluded that the percentage of the classification accuracy is high with the highest accuracy percentage recorded at 98.7% while the lowest was 94.8%.Meanwhile, the average classification accuracy was recorded at 96.9%. The result obtained revealed that the SVM classifiers generated have successfully classified the lesion location correctly according to the lung zones

Applications of Computer Aided Design (CAD) in Medical Image Technology

This paper is submitting the idea of Computer Aided Design (CAD) software application in manipulation of digital x-ray images (DICOM). The study also discusses the concept of raster and vector images as DICOM images will be referred to as raster graphic and CAD as vector graphic. Vectorizing allows an image to be more flexible and can be manipulated so that more information can be loaded into it. As such, it is not impossible that vectorizing method can also be performed on medical images using CAD software such as AutoCAD, Solidworks and others. A DICOM image with DCM format is converted to JPEG format using Medweb software. Then, by using Image2CAD software, the x-ray image is converted to DXF format. The results showed that patient's x-ray image can be manipulated by using AutoCAD software. Study shows that CAD is not only used in the manufacturing field, but it can also be used in the medical field as well

Multiclass Classification Application using SVM Kernel to Classify Chest X-ray Images Based on Nodule Location in Lung Zones

Support Vector Machine (SVM) has long been known as an excellent approach for image classification. While many studies have reported on its achievement, yet it still weak to handle multiclass classification problem because it is originally designed as a binary classification technique. It is challenging task to transform SVM to solve multiclass problems like classifying chest X-ray images based on the lung zone location. Classified X-ray images improved image retrieval hence reducing time taken to assessed back the images. Realizing this difficulties, therefore, we proposed an application method for multiclass classification using SVM kernel to classify chest X-ray images based on nodule location in lung zones. The multiclass classification experiment is performed using four popular SVM kernels namely linear, polynomial, radial based function (RBF) and sigmoid. Overall, we obtained high classification accuracy (>90%) for three classifiers that are RBF, polynomial and linear kernel while sigmoid kernel classifier is only moderately good at 82.7% accuracy. Besides, values in the confusion matrices revealed that the RBF and polynomial classifiers managed to classify test data into all classification classes. Conversely, classifiers based on linear and sigmoid kernel have missed at least one classification class. Since each classifier work differently based on their kernel types, we noticed that it is better to view them as a complimentary rather than treating them as competing options. This condition also revealed that we can modify the original SVM classification method to handle multiclass classification problem

Perbandingan Dos Sinaran antara Prosedur Urografi Intravena (IVU) dan Tomografi Berkomputer Helikal Tanpa Kontras (UHCT) Urografi

Urografi intravena (IVU) dan tomografi berkomputer helikal tanpa kontras (UHCT) urografi adalah dua prosedur utama yang akan dijalankan semasa penyiasatan radiologi bagi pengesanan urolitiasis (batu karang) pada sistem genitourinari. Dedahan terhadap sinaran radiasi merupakan faktor kebimbangan utama dalam kedua-dua prosedur. Oleh itu, satu kajian perbandingan dos sinaran telah dijalankan antara prosedur IVU dan UHCT urografi di samping menentukan faktor dedahan optimum bagi kedua-dua prosedur tersebut. Kajian ini telah dijalankan ke atas fantom antropomorfi k seluruh tubuh mengikut protokol sebenar bagi prosedur UHCT urografi dan penghasilan radiografi bersiri berserta dengan pemberian media berkontras bagi prosedur IVU. Sebanyak tiga parameter dedahan voltan tiub digunakan iaitu 75 kVp, 80 kVp dan 85 kVp bagi prosedur IVU dan 100 kVp, 120 kVp dan 140 kVp bagi prosedur UHCT urografi . Hasil dos sinaran bagi prosedur IVU yang diperolehi adalah 1.40 mSv, 2.10 mSv dan 2.79 mSv bagi 75 kVp, 80 kVp dan 85 kVp. Manakala bagi prosedur UHCT urografi , sebanyak 0.76 mSv, 1.32 mSv dan 1.82 mSv dos sinaran direkodkan bagi 100 kVp, 120 kVp dan 140 kVp. Hasil kualiti imej optimum adalah menggunakan dedahan sebanyak 85 kVp bagi prosedur IVU dan 120 kVp bagi prosedur UHCT urografi . Kesimpulannya, walaupun tidak terdapat perbezaan signifi kan, dos sinaran yang terhasil daripada prosedur IVU adalah kekal lebih tinggi daripada prosedur UHCT urografi

Reka bentuk implan sendi pinggul dan penggunaannya dalam persekitaran digital (Design of hip joint implan and its use in digital environmental)

Bidang ortopedik pada masa kini menjadi semakin penting kerana bilangan pesakit yang menghidapi penyakit ‘osteoporosis’ meningkat setiap tahun. Cara pengecaman secara konvensional masih digunakan untuk mencari implan yang sesuai bagi pesakit. Oleh itu satu kaedah secara digital perlu dibangunkan supaya proses pengecaman saiz implan dapat dilaksanakan dengan berkesan. Kertas ini menunjukkan bagaimana implan sendi pinggul direka bentuk untuk digunakan dalam persekitaran digital. Templat implan yang digunakan secara manual oleh pihak PPUKM telah digunakan sebagai asas kepada reka bentuk implan sendi pinggul. Templat ini direka bentuk semula dengan menggunakan perisian AutoCad 2008. Templat yang dihasilkan dalam format AutoCAD ditukarkan ke format imej JPEG supaya ia boleh digunakan dalam perisian Photoshop untuk tujuan pewarnaan dan penskalaan. Implan digital ini kemudiannya telah diuji dengan menggunakan imej sinar-X pesakit yang disediakan oleh PPUKM. Keputusan menunjukkan implan sendi pinggul digital yang dihasilkan sangat sesuai dan boleh digunakan dalam persekitaran digital

Dural Metastasis Mimicking Meningioma: An Interesting Case

Dural metastasis is a rare entity in clinical practice. We report a case of dural metastasis secondary to thyroid carcinoma, which on both preoperative CT and MRI and at surgery had the typical appearance of a meningioma. Histopathological findings confirmed metastatic follicular thyroid carcinoma as a primary site. Although rare, dural metastases can mimic a meningioma. Our experience in this case has led us to consider metastasis as a differential diagnosis even when a meningioma is suspected. We believe that reporting of the case of dural metastasis mimicking a meningioma may help clinicians in future