18544883. Image Processing Device, Image Processing Method, and Storage Medium simplified abstract (NEC Corporation)
Contents
- 1 Image Processing Device, Image Processing Method, and Storage Medium
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Image Processing Device, Image Processing Method, and Storage Medium - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Image Processing Device, Image Processing Method, and Storage Medium
Organization Name
Inventor(s)
Kazuhiro Watanabe of Tokyo (JP)
Image Processing Device, Image Processing Method, and Storage Medium - A simplified explanation of the abstract
This abstract first appeared for US patent application 18544883 titled 'Image Processing Device, Image Processing Method, and Storage Medium
Simplified Explanation
The image processing device X includes an acquisition means X, a variation detection means X, a selection means X, and a lesion detection means X. The acquisition means X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means X detects a degree of variation between the endoscopic images. The selection means X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means X detects the lesion based on a selection model that is either the first model or the second model selected.
- Acquisition means X: Acquires endoscopic images of an examination target.
- Variation detection means X: Detects the degree of variation between endoscopic images.
- Selection means X: Selects a model based on the degree of variation for lesion inference.
- Lesion detection means X: Detects lesions based on the selected model.
Potential Applications
This technology can be applied in medical settings for more accurate and efficient detection of lesions during endoscopic procedures.
Problems Solved
1. Improved accuracy in lesion detection during endoscopic examinations. 2. Enhanced efficiency in diagnosing and treating medical conditions.
Benefits
1. Early detection of lesions leading to timely medical interventions. 2. Reduction in misdiagnosis and unnecessary procedures. 3. Enhanced patient outcomes and overall healthcare efficiency.
Potential Commercial Applications
Optical imaging companies can integrate this technology into their endoscopic devices to offer advanced lesion detection capabilities to healthcare providers.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in medical imaging for lesion detection, but the specific approach outlined in this patent application may be novel in its focus on selecting different models based on the degree of variation in endoscopic images.
Unanswered Questions
== How does the device determine the optimal number of endoscopic images for lesion inference in each model? The patent abstract does not provide details on the specific criteria or algorithm used to determine the optimal number of endoscopic images for lesion inference in each model.
== What is the accuracy rate of lesion detection using the first model compared to the second model? The abstract does not mention any data or results regarding the accuracy rates of lesion detection using the first model versus the second model. Further information on the comparative performance of these models would be beneficial for understanding the effectiveness of this technology.
Original Abstract Submitted
The image processing device X includes an acquisition means X, a variation detection means X, a selection means X, and a lesion detection means X. The acquisition means X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means X detects a degree of variation between the endoscopic images. The selection means X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means X detects the lesion based on a selection model that is either the first model or the second model selected.
