Use of neural networks for feature based recognition of liver region on CT images

Husain, Syed Afaq; Shigeru, E.

doi:10.1109/nnsp.2000.890163

Cited by 10 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A probabilistic neural network was used as a classifier. A similar system, but one able to distinguish a healthy liver and liver disease, was later presented by Husain et al (2000).…”

Section: Review Of Image-based Cad Systems Based On Liver Ct Imagesmentioning

confidence: 99%

Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features

Duda¹,

Krȩtowski²,

Bézy-Wendling³

2013

Studies in Logic, Grammar and Rhetoric

View full text Add to dashboard Cite

Abstract. In this work, a system for the classification of liver dynamic contestenhanced CT images is presented. The system simultaneously analyzes the images with the same slice location, corresponding to three typical acquisition moments (without contrast, arterial-and portal phase of contrast propagation). At first, the texture features are extracted separately for each acquisition moment. Afterwards, they are united in one "multiphase" vector, characterizing a triplet of textures. The work focuses on finding the most appropriate features that characterize a multi-image texture. At the beginning, the features which are unstable and dependent on ROI size are eliminated. Then, a small subset of remaining features is selected in order to guarantee the best possible classification accuracy. In total, 9 extraction methods were used, and 61 features were calculated for each of three acquisition moments. 1511 texture triplets, corresponding to 4 hepatic tissue classes were recognized (hepatocellular carcinoma, cholangiocarcinoma, cirrhotic, and normal). As a classifier, an adaptive boosting algorithm with a C4.5 tree was used. Experiments show that a small set of 12 features is able to ensure classification accuracy exceeding 90%, while all of the 183 features provide an accuracy rate of 88.94%.

show abstract

“…A probabilistic neural network was used as a classifier. A similar system, but one able to distinguish a healthy liver and liver disease, was later presented by Husain et al (2000).…”

Section: Review Of Image-based Cad Systems Based On Liver Ct Imagesmentioning

confidence: 99%

Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features

Duda¹,

Krȩtowski²,

Bézy-Wendling³

2013

Studies in Logic, Grammar and Rhetoric

View full text Add to dashboard Cite

show abstract

“…Sınıflayıcı tabanlı teknikler, yukarıda belirtilen yöntemlerin çözemedikleri zorluklarla başa çıkabilen alternatif yöntemlerdir [3,4,[36][37][38]. Ancak, yeterli başarımı sağlayabilmeleri için iki noktaya özellikle dikkat edilmesi gerekmektedir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

Selvi¹,

Selver²,

Kavur³

et al. 2015

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

ÖZETTıbbi görüntüleme ile anatomi hakkında ayrıntılı bilgi elde edinilebildiğinden, tanı amaçlı görüntüleme günümüzde birçok açıdan önem kazanmıştır. Görüntüleme cihazları tarafından sunulan verilerin fazlalığı ve çeşitliliği nedeniyle, tüm veri yerine görüntülerde sadece ilgilenilen dokunun belirlenerek ayrılması (Bölütlenmesi) sağlanabilir. Elcil yöntemler ile bölütleme yorucu, zaman alıcı ve deneyim gerektiren bir işlem olduğundan, otomatik yordamlara gereksinim duyulmaktadır. Geliştirilen yordamların klinik koşullarında kullanılabilmesi içinse yüksek başarıma sahip sonuçlar üretmeleri gerekmektedir. Manyetik Rezonans (MR) görüntülerinden batın bölgesindeki organlarının bölütlenmesi pek çok zorluk içeren bir uygulama alanıdır ve bu konudaki çalışmalar sınırlı sayıdadır. Batın bölgesinde yer alan, karaciğer, böbrekler, dalak, pankreas, safra kesesi gibi organların MR görüntüleri kullanılarak ileri seviye tıbbi analizi ve üç boyutlu incelenmesi pek çok tıbbi prosedür için mecburi olduğundan, bu çalışmada, ilgili organların bölütlenmesinde yukarıda belirtilen zorluklara karşı gürbüz, bölütlenecek organın özellikleri ve organların birbirleriyle olan ilişkilerini (konum vb.) göz önüne alan bir sistem geliştirilmiştir. Geliştirilen sistem farklı MR sekansları ile elde edilen görüntülere uygulanarak elde edilen sonuçlar tartışılmıştır.Anahtar Kelimeler: Bölütleme, MR, hiyerarşik sınıflama, batın SEGMENTATION OF ABDOMINAL ORGANS FROM MR IMAGES USING MULTI-LEVEL HIERARCHICAL CLASSIFICATION ABSTRACTMedical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data (segmentation). Since manual segmentation on each slice is time consuming, tedious and operator dependent, automatic tools and techniques are needed. Segmentation of abdominal organs is a very challenging field of application due to overlapping intensity ranges of the organs, variations in human anatomy and pathology and the number of studies is very limited for Magnetic Resonance (MR), which is a relatively newer and rapidly developing imaging modality.Since it is obligatory to analyze and visualize MR images of abdominal organs (i.e. liver, right/left kidneys, spleen, pancreas, gall bladder) for several medical procedures, the main goal of this paper is to design and develop a segmentation system (method+software), which is robust to the challenges mentioned above, adaptive to the properties of the abdominal organs as well as to the interrelationships of these organs.

show abstract

“…Algorithms used for liver segmentation include grey level evaluation [6,11,[24][25][26][27], clustering [17,[28][29][30], region-based method [31,32], Snakesbased method [33], grow-cut [34], graph cuts [15,[35][36][37], level set [16,[38][39][40][41], combinations of different approaches as for example Snakes and grow-cut [33], or graph cut and gradient flow active contour [5], or morphological operations and graph cuts [9,42], grey level and a priori knowledge like CT numbers and location [25], hidden Markov measure field model [18], multi-class smoothed Bayesian classification [20,21], and edge based methods [43]. The use of segmentation algorithm based on priority knowledge about appearance, shape and size of the liver [10,23,[44][45][46][47][48][49][50][51][52] and methods based on neural networks [53,54] have been also proposed.…”

Section: Introductionmentioning

confidence: 99%

Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease

Bliznakova

Kolev²,

Bliznakov³

et al. 2014

JSEA

View full text Add to dashboard Cite

Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing major hepatectomy. The aim of this work is to develop and test a software application for evaluation of the residual function of the liver prior to the intervention of the surgeons. For this purpose, a complete software platform consisting of three basic modules: liver volume segmentation, visualization, and virtual cutting, was developed and tested. Liver volume segmentation is based on a patient examination with non-contrast abdominal Computed Tomography (CT). The basis of the segmentation is a multiple seeded region growing algorithm adapted for use with CT images without contrast-enhancement. Virtual tumor resection is performed interactively by outlining the liver region on the CT images. The software application then processes the results to produce a three-dimensional (3D) image of the "resected" region. Finally, 3D rendering module provides possibility for easy and fast interpretation of the segmentation results. The visual outputs are accompanied with quantitative measures that further provide estimation of the residual liver function and based on them the surgeons could make a better decision. The developed system was tested and verified with twenty abdominal CT patient sets consisting of different numbers of tomographic images. Volumes, obtained by manual tracing of two surgeon experts, showed a mean relative difference of 4.5%. The application was used in a study that demonstrates the need and the added value of such a tool in practice and in education.

show abstract

Use of neural networks for feature based recognition of liver region on CT images

Cited by 10 publications

References 11 publications

Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features

Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease

Contact Info

Product

Resources

About