1 citations · 1 across the 4 of their papers we have counts for
5 papers
Unsupervised denoising for sparse multi-spectral computed tomography
Satu I. Inkinen, Mikael A. K. Brix, Miika T. Nieminen +2
Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, P…
Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee…
Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Objective: To assess the ability of imaging-based deep learning to predict radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee latera…
A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on s…
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
Neslihan Bayramoglu, Aleksei Tiulpin, Jukka Hirvasniemi +2
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the abil…