Image patch-based method for automated classification and detection of focal liver lesions on CT

Mustafa Safdari, Raghav Pasari, Daniel Rubin, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan windflow moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

Original languageEnglish
Title of host publicationMedical Imaging 2013
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - 2013
EventMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: 12 Feb 201314 Feb 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering


ConferenceMedical Imaging 2013: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • CT
  • Classification
  • Detection
  • Liver lesions
  • Visual words

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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