Investigation of PET/MRI image fusion schemes for enhanced breast cancer diagnosis

Baum, Karl G.; Rafferty, Kimberly; Helguera, María; Schmidt, Evan

doi:10.1109/nssmic.2007.4436944

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Similar to CT and MRI, a major application of PET is in radiology studies for brain diagnosis and treatment [47,135,48,208,140,143]. There are a wide range of the application of image fusion using PET, some of which are for cancer treatments [51,223,220,162,222,209,225,229,131], image segmentation and integration [51,259], 3D tumor simulation [191], gynecological cancer diagnosis [190], inertial electrostatic confinement fusion [270], gross tumor volume detection [200,271], diagnosis of local recurrence of rectal cancer [214], tumor detection and treatment [246,220], pediatric solid extracranial tumors [251], telemedicine [201], breast cancer detection [184,252,253], oral cancer treatment [254], lung cancer diagnosis [256,272], cervical cancer treatment [202], esophageal cancer diagnosis [260], and pancreatic tumors characterization [207,261].…”

Section: Positron Emission Tomographymentioning

confidence: 99%

“…There is increased interest in using fusion techniques to improve the imaging quality. The use of PET data in combination with some of the existing modalities using the image fusion techniques include MRI-CT-PET-SPECT-DSA-MEG [47,135], MRI-CT-PET [51,191,200,201,202], MRI-SPECT-PET [48], MRI/CT-PET-SPECT [190], MRI-PET [207,208,140,184,209,143,130,131], FDG-PET-CT [229,251,253], PET-CT [273,248,214,246,220,221,239,29,223,68,254,256,252,222,76,274,261], FDG-PET [272], and PET-CT-ultrasound [260].…”

Section: Positron Emission Tomographymentioning

confidence: 99%

“…bowel) and pathological uptake. The modalities that has been used to study breast include MRI [183,184,185], Mammogram [183,280,185], Infrared [38,280], Ultrasound [280], Microwave [284,285], PET [184,252], SPECT [217,257] and CT [252,257]. The image fusion applications targeted on breast include prediction breast cancer tumors [21,108,44], breast surgery [309], ultrawideband breast cancer detection [285], and breast cancer detection in premenopausal women [310].…”

Section: Breastmentioning

confidence: 99%

“…There are methodological advancements in the fusion of MR images such as structure similarity match measure (SSIM) [178] that can improve the accuracy of these applications. Other potential applications that incorporate MRI based fusion include image regeneration [179,180,143], potential field visualization [181], lung/liver diagnosis [182], tissue classification [105,20], breast cancer assessment [183,184,185], surgical planning and training [186], multi-dimensional visualization [124], extraction of shape, color and structure of specimen [187], visualization and pattern recognition [188], image registration [125,152,53,127,189,132], MRI guided treatment [152,153], gynecological cancer diagnosis [190] and 3D tumor simulation [191].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Positron Emission Tomographymentioning

confidence: 99%

Section: Positron Emission Tomographymentioning

confidence: 99%

Section: Breastmentioning

confidence: 99%

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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“…Image processing methods, such as image fusion, are increasing in importance in modern medicine and health care for fusing multimodal medical images, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), and positron emission tomography (PET) [1]- [3]. Currently, research in the handling and investigation of medical data has begun to flourish.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Based on Sugeno's Intuitionistic Fuzzy Sets

Tirupal¹,

Mohan²,

Kumar³

2017

ETRI Journal

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Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non‐membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

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