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Intelligent Image Processing Lab
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A lab that builds the ability to see and understand things into computers through image processing.
AboutAbout the lab
Giving computers the ability to see and know things.
The Intelligent Image Processing Lab conducts research aimed at enabling computers to see things as humans do and understand what they are.
The lab develops theories and algorithms for recognizing and understanding scenes and objects in digital images, and also develops programs that implement them.
Students are divided into groups for medical image processing, support for visually impaired people, and material image analysis, and each proceeds with research at their own pace.
About once a week, there are sessions to report research progress, and also opportunities to explain English-language papers to one another.
Key terms
- Smoothing
- Processing that removes noise from an image
- Binarization
- Processing that divides an image into two by brightness to extract regions such as opacities
- Mathematical morphology
- Processing that shapes the form of a three-dimensional region
ResearchResearch themes
This lab aims to build the ability to "see and know things" into computers. It develops theories and algorithms for recognizing and understanding scenes and objects from digital images and implements them as programs, using medical images, stereo images, and the vision system of a guide dog robot as subjects.
*Illustrative imageLesion detection system to support doctors
To reduce the burden on doctors who interpret medical images, the lab develops methods in which a computer recognizes lesions and provides information. Chest X-ray CT images are processed with smoothing, binarization, labeling, and other steps, and lesions are detected by measuring the size, shape, and density of opacities.
*Illustrative imageImage filters for finding lung cancer
To distinguish round lung cancer opacities from linear blood vessel opacities in chest X-ray CT images, the lab uses a Disk filter and a Ring filter. Opacities for which the difference in the drop height between the two filters is large are detected as cancer.
*Illustrative imageExtracting the lung organ from CT images
To know exactly the range where a lesion lies, the lab develops a method for extracting the lung region from chest X-ray CT images. The lung region is first roughly extracted by binarization, and its shape is then refined with mathematical morphology to extract it accurately.
*Illustrative imageDistinguishing cancer from blood vessels with 3D models
Focusing on the three-dimensional structural difference that lung cancer grows spherically in its early stage while blood vessels are tubular, the lab represents cancer with a spherical model and blood vessels with a model combining cylinders, and distinguishes them by matching against image data. A distribution model of pulmonary vessel diameter is also reflected in the vessel model.
MembersFaculty
The members of the Intelligent Image Processing Lab consist of faculty members and doctoral, master's, and undergraduate students.As of 2026
- ProfessorHotaka Takizawa
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Robot Equipped with Cameras and Sensors” Status 401 / CC0 (cropped)
- Lesion detection system to support doctors: “Diagnostic Imaging and Radiology Facility in Navi Mumbai, Maharashtra, India” Goleisureintl / CC BY-SA 4.0 (cropped)
- Image filters for finding lung cancer: “CT Brain Scan” Goleisureintl / CC BY 4.0 (cropped)
- Extracting the lung organ from CT images: “Veterinary CT Scanner (NIH BioArt )” Courtesy of NIAID Malcolm Houston / Public domain (cropped)
- Distinguishing cancer from blood vessels with 3D models: “Preoperative planning with 3D printed model” Jan Witowski / CC BY-SA 4.0 (cropped)
- Related lab (Computer Vision Lab): “Nikon 1 mount” Ivan Radic / CC BY 2.0 (cropped)
- Related lab (Program Logic Lab): “MIDI note player source code” Yadarwish / CC BY-SA 3.0 (cropped)
- Related lab (Operating Systems and System Software Lab): “Bacloud.com data center 2” Fleshas / CC BY-SA 4.0 (cropped)
- Related lab (Multimedia Lab): “Muscle Shoals Sound Studio, recording booth, 2025” Peter K Burian / CC BY 4.0 (cropped)
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