1 research outputs found
Computer analysis of ultrasound images of thyroid nodules, focusing on their sonographic features and cytological findings.
UltrazvukovĂ© zobrazovánĂ patřà mezi základnĂ vyšetĹ™enĂ uzlĹŻ ve štĂtnĂ© Ĺľláze, na jejichĹľ základÄ› se rozhoduje, zda pacient podstoupĂ cytologickĂ© vyšetĹ™enĂ, kterĂ© je hlavnĂm podkladem pro rozhodovánĂ o pĹ™ĂpadnĂ©m chirurgickĂ©m odstranÄ›nà štĂtnĂ© Ĺľlázy. CytologickĂ© vyšetĹ™enĂ má ale bohuĹľel omezenou specificitu a pĹ™Ăpadná operace s sebou nese rizika. Proto jsou hledány dalšà metody, kterĂ© by byly schopny vnĂ©st do diagnostiky vĂce jistoty. Jednou z novĂ˝ch metod je poÄŤĂtaÄŤová podpora diagnostiky (CAD), která pomocĂ analĂ˝zy obrazu a strojovĂ©ho uÄŤenĂ vykazuje pomÄ›rnÄ› slibnĂ© vĂ˝sledky. V tĂ©to práci pĹ™edstavujeme dva do urÄŤitĂ© mĂry podobnĂ©, pĹ™esto však odlišnĂ©, CAD pĹ™Ăstupy. PrvnĂ pĹ™Ăstup spoÄŤĂvá v analĂ˝ze celĂ˝ch uzlĹŻ pomocĂ Segmentation Based Fractal Texture Analysis (SFTA) algoritmu, kterĂ˝ rozkládá obraz na jednotlivá šedotĂłnová pásma pomocĂ metody binárnĂ stack-dekompozice. PomocĂ tohoto pĹ™Ăstupu bylo na datovĂ©m souboru 40 snĂmkĹŻ hodnocenĂ˝ch metodou kĹ™ĂĹľovĂ© validace dosaĹľeno pĹ™esnosti 92,5 % pĹ™i pouĹľitĂ náhodnĂ˝ch lesĹŻ a 95 % pĹ™i pouĹľitĂ support vector machines (SVM). DruhĂ˝ CAD pĹ™Ăstup vycházĂ takĂ© z metody vĂcenásobnĂ©ho prahovánĂ obrazu, ale s tĂm rozdĂlem, Ĺľe z jednotlivĂ˝ch šedotĂłnovĂ˝ch pásem je extrahováno vÄ›tšà mnoĹľstvĂ prediktorĹŻ popisujĂcĂch binárnĂ texturu a dále pak, Ĺľe analĂ˝za neprobĂhá na uzlu jako celku, ale...Ultrasound imaging is one of the fundamental examinations of thyroid nodules, determining whether a patient undergoes a cytological examination, which is essential for the decision on a possible thyroid surgery. Unfortunately, the cytological examination has limited specificity and potential surgery carries risks. Therefore, other diagnostic methods are being sought with hope that they will be able to bring more certainty into diagnostics. One of the new methods is computer-aided diagnosis (CAD), which exhibits promising results using image analysis and machine learning. In this study, we present two somewhat similar, yet different, CAD approaches. The first approach is based on analysing entire nodules using a Segmentation Based Fractal Texture Analysis (SFTA) algorithm that splits the image into individual grayscale bands. Using this approach, we have achieved an accuracy of 92.4% using random forests (RF) and 95% using support vector machines (SVM) on a data set of 40 images evaluated by the cross-validation method. The second CAD approach is also based on the method of multiple image thresholding, but the difference is, that a larger number of predictors describing the binary texture are extracted from the individual grayscale bands. Furthermore, the analysis did not take place on whole nodules, but on...Institute of Biophysics and Informatics First Faculty of Medicine Charles UniversityĂšstav biofyziky a informatiky 1. LF UK1. lĂ©kaĹ™ská fakultaFirst Faculty of Medicin