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Estimation of kidney tumor volume in CT images using medical image segmentation techniques
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Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentation method of gray level CT images. The segmentation process is performed by using the Fuzzy C-Means (FCM) clustering method to detect and segment kidney CT images for the kidney region. The propose method is started with pre-processing of the kidney CT image to separate the kidney from the abdomen CT and to enhance its contrast and removing the undesired noise in order to make the image suitable for further processing. The resulted segmented CT images, then used to extract the tumor region from kidney image defining the tumor volume (size) is not an easy task, because the 2D tumor shape in the CT slices are not regular. To overcome the problem of calculating the area of the convex shape of the hull of the tumor in each slice, we have used the Frustum model for the fragmented data.

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
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Publication Date
Tue Jun 03 2025
Journal Name
Sar Journal Of Pathology And Microbiology
The Relation between Tumor Infiltrating Lymphocyte and Classical Tumor Staging in Colorectal Carcinoma (Semi-Quantitative Study by Immun histochemistry in a Group of Iraqi Patients)
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Inflammatory response had a role in cancer progression, presence of noticeable inflammation within the tumor and its margin may play an important prognostic role in colorectal carcinoma.

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Publication Date
Sun Dec 05 2010
Journal Name
Baghdad Science Journal
Effect of temperature on viability of human hydatid cyst & study the volume of hydatid cyst
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The hydatid materials were collected and studied, so they were contained 50 fertile human hydatid cases {33 (66%) females and 17 (34%) males}. They were collected from Al-Ramadi General Hospital during the period from December, 2003 to July, 2004 .Cysts were observed in 40 (80%) from the liver, 5 (10%) from the lungs, 3 (6%) from the kidney and 2 (4%) cysts from urinary bladder. The specimens were taken from patients of different ages. The in vitro viability of protoscoleces was assessed on the basis of flame cell activity and eosein exclusion, which were considered as criteria to determine the death or viability of protoscoleces. In addition to this movement (flame cell activity), another motility like constriction – relaxatio

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Publication Date
Fri Apr 30 2010
Journal Name
Journal Of Applied Computer Science & Mathematics
Image Hiding Using Magnitude Modulation on the DCT Coefficients
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In this paper, we introduce a DCT based steganographic method for gray scale images. The embedding approach is designed to reach efficient tradeoff among the three conflicting goals; maximizing the amount of hidden message, minimizing distortion between the cover image and stego-image,and maximizing the robustness of embedding. The main idea of the method is to create a safe embedding area in the middle and high frequency region of the DCT domain using a magnitude modulation technique. The magnitude modulation is applied using uniform quantization with magnitude Adder/Subtractor modules. The conducted test results indicated that the proposed method satisfy high capacity, high preservation of perceptual and statistical properties of the steg

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Publication Date
Sun Dec 03 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
Studying Hueckel edge detector using binary step edge image
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Publication Date
Wed Mar 23 2011
Journal Name
Ibn Al- Haitham J. For Pure & Appl. Sci.
Image Compression Using Proposed Enhanced Run Length Encoding Algorithm
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In this paper, we will present proposed enhance process of image compression by using RLE algorithm. This proposed yield to decrease the size of compressing image, but the original method used primarily for compressing a binary images [1].Which will yield increasing the size of an original image mostly when used for color images. The test of an enhanced algorithm is performed on sample consists of ten BMP 24-bit true color images, building an application by using visual basic 6.0 to show the size after and before compression process and computing the compression ratio for RLE and for the enhanced RLE algorithm.

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Merge Operation Effect On Image Compression Using Fractal Technique
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Fractal image compression gives some desirable properties like fast decoding image, and very good rate-distortion curves, but suffers from a high encoding time. In fractal image compression a partitioning of the image into ranges is required. In this work, we introduced good partitioning process by means of merge approach, since some ranges are connected to the others. This paper presents a method to reduce the encoding time of this technique by reducing the number of range blocks based on the computing the statistical measures between them . Experimental results on standard images show that the proposed method yields minimize (decrease) the encoding time and remain the quality results passable visually.

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Publication Date
Tue Dec 31 2024
Journal Name
Journal Of Soft Computing And Computer Applications
Enhancing Image Classification Using a Convolutional Neural Network Model
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In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

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Publication Date
Wed Jul 29 2020
Journal Name
Iraqi Journal Of Science
Fractal Image Compression Using Block Indexing Technique: A Review
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Fractal image compression depends on representing an image using affine transformations. The main concern for researches in the discipline of fractal image compression (FIC) algorithm is to decrease encoding time needed to compress image data. The basic technique is that each portion of the image is similar to other portions of the same image. In this process, there are many models that were developed. The presence of fractals was initially noticed and handled using Iterated Function System (IFS); that is used for encoding images. In this paper, a review of fractal image compression is discussed with its variants along with other techniques. A summarized review of contributions is achieved to determine the fulfillment of fractal ima

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Publication Date
Fri Jun 23 2023
Journal Name
Al-mustansiriyah Journal Of Science
Image Encryption Using New Non-Linear Stream Cipher Cryptosystem
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In this paper, we designed a new efficient stream cipher cryptosystem that depend on a chaotic map to encrypt (decrypt) different types of digital images. The designed encryption system passed all basic efficiency criteria (like Randomness, MSE, PSNR, Histogram Analysis, and Key Space) that were applied to the key extracted from the random generator as well as to the digital images after completing the encryption process.