A module for visualisation and analysis of digital images in DICOM file format

Rusev, R. M.

doi:10.1145/973620.973672

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…DICOM format is used extensively in CT, MR and ultrasound devices and combines images and metadata to create a rich description of a medical imaging procedure. Each DICOM file header contains a Service-Object Pair (SOP) instance related to Information Object Definition (IOD) [28] which is useful for voxelization of scanned organ. The voxel-based tomographic computational model can be constructed by stacking up the medical images embedded within the DICOM files [29].…”

Section: A Dicom File Formatmentioning

confidence: 99%

SAR Analysis for handheld mobile phone using DICOM based voxel model

Ali¹,

Ray

2013

J. Microw. Optoelectron. Electromagn. Appl.

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In this paper, Specific Absorption Rates (SAR) inside the human head and hand have been analyzed for a handheld mobile phone operated at GSM 900 band. Both the head and hand are modeled electrically using Digital Imaging and Communication in Medicine (DICOM) formatted CT Scan voxel data considering the electrical parameters of different internal anatomical structures. Three-dimensional Finite Difference in Time Domain (FDTD) method has been used to simulate SAR induced in the head and hand. Maximum peak 1-g and 10-g SARs of 1.33 W/kg and 0.388 W/kg are found at antenna resonance frequency of 930 MHz for 0.6 W applied input power which are below the limits set by ANSI/IEEE and FCC, respectively. Index Terms-FDTD, near-field, peak 1-g and 10-g SARs, DICOM data, CT scan, human head and hand models.

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Section: A Dicom File Formatmentioning

confidence: 99%

SAR Analysis for handheld mobile phone using DICOM based voxel model

Ali¹,

Ray

2013

J. Microw. Optoelectron. Electromagn. Appl.

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show abstract

“…The header contains a Service-Object Pair (SOP) instance related to Information Object Definition (IOD) (29) which is useful for voxelization of scanned organ. The voxel-based tomographic computational model can be constructed by stacking up the medical images embedded within the DICOM files (23).…”

Section: Read Ct/mr Scan Based Dicom Datamentioning

confidence: 99%

Offline RF thermal ablation planning using CT/MRI scan data

Ali¹,

Ray

2015

The Egyptian Journal of Radiology and Nuclear Medicine

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In this paper, steady state temperature variations in the human brain due to different electrical and physical changes have been studied considering different thermal ablation treatment requirements using RF probe. Initially, a subject specific voxel-based electrical model has been constructed from Digital Imaging and Communication in Medicine (DICOM) formatted Computed Tomography (CT) or Magnetic Resonance (MR) based image stacks with different pixel characteristics using the Hounsfield unit extraction technique. This subject specific electrical model that consists of different dielectric constant and conductivity, considering different anatomical organs and tissues is simulated using commercially available finite integral technique (FIT) based EM simulation software CST Microwave Studio Ò . This study clearly shows the possibility of subject specific precise offline microwave thermal ablation treatment planning.

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“…These four criteria are made as input to the neural network (which is the smart diagnosis in the second part) to classify cervical cells to three stages namely normal, Low-grade Squamous Intraepithelial Lesions, (LSIL) and High-grade Squamous Intraepithelial Lesions (HSIL). Nevertheless, the NeuralPap system processes image at a grey scale, despite image processing through pseudocolouring becoming increasingly popular in medical imaging applications [18][19][20][21][22][23]. This is owing to the fact that the human visual system is more sensitive to colours from monochromatic images.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Sulaiman¹,

Isa²,

Othman³

et al. 2015

Procedia Computer Science

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Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

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A module for visualisation and analysis of digital images in DICOM file format

Cited by 6 publications

References 3 publications

SAR Analysis for handheld mobile phone using DICOM based voxel model

SAR Analysis for handheld mobile phone using DICOM based voxel model

Offline RF thermal ablation planning using CT/MRI scan data

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

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