A soft-segmentation visualization scheme for magnetic resonance images

Mehta, Shashi Bhushan; Chaudhury, Santanu; Bhattacharyya, Asok; Jena, Amarnath

doi:10.1016/j.mri.2005.05.003

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Many researchers have investigated the relationship between window settings and the form of nodules, and have determined that the adjustment of the window width and window level can highlight tissue boundaries, and individualized window widths and levels can provide more precise detail. 7,8 In this study, we investigated the relationship between the window width at which lesions were no longer apparent and the invasiveness and prognosis of lung adenocarcinoma, and found that window settings could assist in the differential diagnosis of subtypes of lung adenocarcinoma. Window width is the range of CT value shown on CT images; the range of CT value decreases with the decrease of window width, which can explain the difference between visibility of preinvasive and invasive lesions.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of the invasiveness of lung adenocarcinoma manifesting as ground glass opacities on high‐resolution computed tomography

et al. 2015

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BackgroundTo explore the diagnostic method in assessing the malignancy of pulmonary adenocarcinoma characterized by ground glass opacities (GGO) on computed tomography (CT).MethodsPreoperative CT data for preinvasive and invasive lung adenocarcinomas were analyzed retrospectively. GGO lesions that were detected on lung windows but absent using the mediastinal window were subject to adjustment of the window width, which was reduced with the fixed interval of 100 HU until the lesions were no longer evident, with a fixed mediastinal window level of 40 HU. The shape, smoking habits, size of the lesion on the lung window, and window width at which lesions disappeared were compared and receiver operating characteristic curves were used to determine the optimal cut‐off of the lesion size and window width to differentiate between these invasive and preinvasive lesions.ResultsOf the 209 lung adenocarcinomas, 102 were preinvasive (25 atypical adenomatous hyperplasia and 77 adenocarcinoma in situ), while 107 were invasive (78 minimally invasive adenocarcinoma and 29 invasive adenocarcinoma). The shape, lesion size, and window width at which lesions were no longer evident differed significantly between the two groups (P < 0.05). The size of 8.9 mm and a window width of 1250 HU were the optimal cut‐off to differentiate between preinvasive and invasive lesions.ConclusionThe shape, size of the lesion, and window width on high‐resolution CT may be useful in assessing the invasiveness of lung adenocarcinoma that manifests as GGO. Irregular lesions that disappear at window width <1250 HU, with a diameter of > 8.9 mm are more likely to be invasive.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of the invasiveness of lung adenocarcinoma manifesting as ground glass opacities on high‐resolution computed tomography

et al. 2015

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“…Automated methods for determining the window parameters have aimed at global contrast enhancement. Techniques using spatial, anatomical and histogram information [1] as well as pseudo colorisation (of segmented results) [6] have been proposed. In the latter case, different tissue classes for T2 weighted MR images are identified from the histogram and the pseudo-colorisation is done using fuzzy membership functions.…”

Section: Introductionmentioning

confidence: 99%

Auto-windowing of ischemic stroke lesions in diffusion weighted imaging of the brain

Mujumdar

Sivaswamy

Kishore

et al. 2013

2013 Indian Conference on Medical Informatics and Telemedicine (ICMIT)

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Diffusion Weighted Magnetic Resonance Imaging (DWI) is routinely used for early detection of cerebral ischemic changes in acute stroke. Fast acquisition with a standard echoplanar imaging technique generally compromises the image signal-to-noise ratio and in-plane resolution resulting in a reduction of the conspicuity and definition of lesions in the acquired data when viewed on a standard 8-bit display. We present a novel method for automatically and adaptively determining the window settings that enhance the contrast of the image relative to the ischemic lesions. The method performs a coarse segmentation of the lesions followed by contrast-to-noise ratio based computation of the optimal window parameters. The proposed method was tested on 24 datasets acquired with different protocols. The contrast improvement of the lesions is validated through a mirror region of interest analysis and by using the contrast improvement ratio metric. The average obtained improvement in contrast ranges from 25% to 60%. Preliminary results of segmentation showed a good reduction in the false positives and improvement in the lesion boundaries. A perception study of the windowed results against 8 radiologists was conducted. Reduction of 14.17% in the mean response time of detection was observed. Statistical analysis performed using t-test validates the reduction in mean response time to be significant. Results presented in the study show promise in the method.

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“…In their approach, competitive neural network specifies the centre of clusters regarding each tissue type and FCM performs soft clustering. Their study was inspired by the method that Mehta et al 15 used in their study.…”

mentioning

confidence: 99%

MRI Segmentation using Fuzzy C-Means and Radial Basis Function Neural Networks

Rasooli¹,

Ashtiyani²,

Birgani³

et al. 2018

Current Science

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Image segmentation is one of the major preprocessing steps of magnetic resonance imaging (MRI) analysis in many medical and research applications. Accurate differentiation between three major soft tissues of the brain -grey matter, white matter and cerebrospinal fluid -is a key step in structural and functional brain analysis, visualization of the brain's anatomical structures and measurement, diagnosis of neurodegenerative disorders and image-guided interventions as well as surgical planning. We propose a new methodological approach in segmentation of MRI images of the brain structure. Although various methods for MRI segmentation have been proposed, improvement of soft, automatic and precise MRI segmentation methods are worth a try. The proposed method has almost the same results as those from recent efforts in this field. However, it performs better in the presence of noise and RF-filed inhomogeneity.

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A soft-segmentation visualization scheme for magnetic resonance images

Cited by 12 publications

References 21 publications

Diagnosis of the invasiveness of lung adenocarcinoma manifesting as ground glass opacities on high‐resolution computed tomography

Diagnosis of the invasiveness of lung adenocarcinoma manifesting as ground glass opacities on high‐resolution computed tomography

Auto-windowing of ischemic stroke lesions in diffusion weighted imaging of the brain

MRI Segmentation using Fuzzy C-Means and Radial Basis Function Neural Networks

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