Computer aids for decision-making in diagnostic radiology—a literature review

Taylor, P.

doi:10.1259/0007-1285-68-813-945

Cited by 49 publications

(17 citation statements)

References 34 publications

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“…By applying the closing morphological operation, we can get rid of the holes form the binary image. Furthermore Opening operation with small structure element can be used to separate some objects that are still connected in small number of pixels [25,26].…”

Section: Morphology-based Operationsmentioning

confidence: 99%

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

Sadek¹,

Abdel‐Khalek²

2014

JSEA

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In 1953, Rènyi introduced his pioneering work (known as α-entropies) to generalize the traditional notion of entropy. The functionalities of α-entropies share the major properties of Shannon's entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision community greatly appealing. In this paper, an efficient and fast entropic method for noisy cell image segmentation is presented. The method utilizes generalized α-entropy to measure the maximum structural information of image and to locate the optimal threshold desired by segmentation. To speed up the proposed method, computations are carried out on 1D histograms of image. Experimental results show that the proposed method is efficient and much more tolerant to noise than other state-of-the-art segmentation techniques.

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Section: Morphology-based Operationsmentioning

confidence: 99%

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

Sadek¹,

Abdel‐Khalek²

2014

JSEA

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“…More recent ones recognise the difficulty of formalising medical knowledge, and offer a range of tools to support a human expert in reflecting on situated experiences, externalising the steps in solving a problem, and making connections between situated and general knowledge. Taylor (Taylor 1995) gives a review of computer aids for decision-making in diagnostic radiology. It covers image databases, numeric and statistical systems, expert systems, image processing systems and image understanding systems.…”

Section: Decision Support Systemsmentioning

confidence: 99%

The use of a computerized brain atlas to support knowledge-based training in radiology

Garlatti

Sharples

1998

Artificial Intelligence in Medicine

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Trainers of radiologists face the particular challenges of teaching normal and abnormal appearance for a variety of imaging modalities, providing access to a large appropriatelyindexed case library, and teaching a consistent approach to the reporting of cases. The computer has the potential to address these issues, to supplement conventional teaching of radiology by providing case-based tutoring and diagnostic support based on a large library of images of normal and abnormal anatomy, fully described in a consistent terminology. This paper presents a new approach to computer-based training in radiology that combines a knowledge-based tutor with an on-line medical atlas. It describes two existing computer systems, The MR Tutor and ATLAS, and discusses the medical, computational, epistemic, and pedagogic issues involved in developing a combined Atlas-Tutor. Integrating an atlas with a training system could significantly improve the teaching and support offered, but practical difficulties include the need to merge knowledge representations and to incorporate techniques for registering atlas plates on images that exhibit abnormalities. The paper addresses these problems, and concludes by indicating how the Atlas-Tutor might be employed in practical radiology training.

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“…For biomedical imaging applications, image segmentation is a founding step in image analysis as it will directly affect the post-processing. It is a crucial component in diagnosis [2] and treatment [3]. The main goal of acute leukemia blood cell segmentation is to extract components such as blast from its complicated blood cells background.…”

Section: Introductionmentioning

confidence: 99%

Hardware Segmentation on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using Xilinx System Generator

ElDahshan¹,

Masameer²,

Youssef³

et al. 2014

ijacsa

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Abstract-Image segmentation is considered the most critical step in image processing and helps to analyze, infer and make decisions especially in the medical field. Analyzing digital microscope images for earlier acute lymphoblastic leukemia diagnosis and treatment require sophisticated software and hardware systems. These systems must provide both highly accurate and extremely fast processing of large amounts of image data. In this work, the hardware segmentation framework for Acute Lymphoblastic Leukemia (ALL) images based color histogram of Hue channel of HSV color space is proposed to segment each leukemia image into blasts and background using Field Programmable Gate Array (FPGA). The main purpose of this work is to implement image segmentation framework in a FPGA with minimum hardware resources and low execution time to be suitable enough for medical applications. Hardware framework of segmentation is designed using Xilinx System Generator (XSG) as DSP design tool that enables the use of Simulink models, implemented in VHDL and synthesized for Xilinx SPARTAN-3E Starter kit (XC3S500E-FG320) FPGA.

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Computer aids for decision-making in diagnostic radiology—a literature review

Cited by 49 publications

References 34 publications

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

The use of a computerized brain atlas to support knowledge-based training in radiology

Hardware Segmentation on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using Xilinx System Generator

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Computer aids for decision-making in diagnostic radiology—a literature review

Cited by 49 publications

References 34 publications

Generalized &lt;i&gt;α&lt;/i&gt;-Entropy Based Medical Image Segmentation

Generalized &lt;i&gt;α&lt;/i&gt;-Entropy Based Medical Image Segmentation

The use of a computerized brain atlas to support knowledge-based training in radiology

Hardware Segmentation on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using Xilinx System Generator

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Product

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Generalized <i>α</i>-Entropy Based Medical Image Segmentation

Generalized <i>α</i>-Entropy Based Medical Image Segmentation