Author: claimsbot

1. A pharmaceutical formulation comprising:
insulin in an amount effective for the control of diabetes; and
glucagon in an amount effective for the prevention of hypoglycemia in a human or other mammal, wherein said pharmaceutical formulation is configured to be administered subcutaneously, and wherein a ratio of insulin to glucagon is about 1 unit of insulin to between more than 40 milliunits to 200 milliunits of glucagon.
2. The pharmaceutical composition of claim 1, wherein the amount of glucagon is between about 50 and 100 milliunits.
3. The pharmaceutical composition of claim 1, wherein the glucagon is a longer-acting form of glucagon.
4. The pharmaceutical composition of claim 3, wherein the longer-acting form of glucagon contains iodine.
5. The pharmaceutical composition of claim 3, wherein the longer-acting form of glucagon contains zinc.
6. The pharmaceutical composition of claim 5, wherein the longer-acting form of glucagon further comprises protamine.
7. A method of treating diabetes in a human or other mammal without inducing hypoglycemia, said method comprising:
administering insulin in an amount therapeutically effective for the control of diabetes, wherein said insulin is in an amount between 0.5 and 20 Units of insulin; and
administering glucagon in time and an amount therapeutically effective for the prevention of hypoglycemia, wherein said glucagon is administered subcutaneously, and wherein the amount of glucagon administered is between more than 5 and less than or equal to 100 ng per kg of patient per minute of desired glucagon effectiveness.
8. The method of claim 7, wherein the amount of glucagon administered is between 6 and less than 18 ng per kg of patient per minute of desired glucagon effectiveness.
9. The method of claim 7, wherein said glucagon is a glucagon with a prolonged duration of action.
10. The method of claim 7, wherein said glucagon is contained in a liposomal formulation.
11. The method of claim 7, wherein said glucagon is contained in a microsphere.
12. The method of claim 7, comprising administering a formulation comprising both insulin and glucagon.
13. The method of claim 7, wherein said insulin and glucagon are contained in a pump that controls administration of a drug to a patient.
14. The method of claim 13, wherein said glucagon is administered simultaneously with insulin.
15. The method of claim 14, wherein a ratio of glucagon to insulin is about more than 40 to 200 milliunits of glucagon to 1 unit of insulin.
16. The method of claim 15, wherein 2 units of insulin are administered.
17. The method of claim 7, wherein 10 units of insulin are administered and between 30 and 90 ng per kg of patient per minute of glucagon are administered subcutaneously.
18. A kit for the administration of glucagon and insulin in amounts to prevent hypoglycemia, said kit comprising:
glucagon;
insulin, wherein said glucagon and insulin are in a ratio of 1-20 units of insulin to 32-480 milliunits of glucagon;
a means for administering glucagon subcutaneously; and
instructions for the administration of insulin and glucagon so that the glucagon prevents a hypoglycemic event.
19. The kit of claim 18, wherein the concentration of glucagon when completely dissolved in the glycerine solution is more than 500 micrograms per milliliter but less than 2000 micrograms per milliliter.
20. The kit of claim 18, wherein said glucagon and insulin are in a ratio of 1-3 units of insulin to 32-96 milliunits of glucagon.
21. The kit of claim 18, wherein the means for administering the glucagon subcutaneously is a pump and said pump is configured to deliver between about 6 to 20 ngkgminute of glucagon.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A waveform equalization apparatus comprising:
an analog-to-digital (AD) converter oversampling a reception signal in synchronization with a base clock signal to generate an AD converted data sequence;
a waveform equalizer performing an arithmetic operation to equalize a waveform regarding the AD converted data sequence in synchronization with the base clock signal, wherein the waveform equalizer includes a detector at an output stage;
a training sequence generator generating a data sequence for training, wherein the data sequence for training is used so as to converge a coefficient used in the arithmetic operation in advance, the data sequence for training is used instead of an output data of the detector, wherein the training sequence generator is used during a training period;
a clock recovery circuit
supplying the base clock signal without executing a clock recovery operation during the training period, and
after termination of the training period, executing the clock recovery operation according to the output data of the detector and generating and outputting the base clock signal;

a plurality of matched filters
receiving the AD converted data sequence, and
executing a filter arithmetic operation to correlate the data sequence for training with the AD converted data sequence in synchronization with a multiphase clock signal having a frequency that corresponds to speed of the reception signal; and

a clock optimization logic supplying the training sequence generator with a predetermined optimum operation clock signal based on the multiphase clock signal and output data of the plurality of the matched filters.
2. The waveform equalization apparatus according to claim 1, wherein:
the multiphase clock signal includes a plurality of clock signals;
the clock optimization logic selects a clock signal corresponding to a matched filter that has a greatest data value among the plurality of clock signals; and
the clock signal corresponding to the matched filter that has the greatest data value is an operation clock signal of the training sequence generator.
3. The waveform equalization apparatus according to claim 1, wherein:
the clock optimization logic includes a clock phase interpolator;
the multiphase clock signal includes a plurality of clock signals;
the clock phase interpolator generates and outputs a clock signal that has a phase difference smaller than a phase difference between two of the plurality of clock signals based on the output data of the matched filters.
4. The waveform equalization apparatus according to claim 1, wherein:
the clock optimization logic includes a memory portion storing the output data from each of the matched filters;
the multiphase clock signal includes a plurality of clock signals;
each of the matched filters enables to receive the plurality of clock signals by switching in time division as an operation clock signal; and
the clock optimization logic processes the output data of the matched filters, the output data being read out from the memory portion.
5. The waveform equalization apparatus according to claim 4, wherein:
the matched filters only include a first matched filter and a second matched filter;
the multiphase clock signal include a first clock signal, a second clock signal, a third clock signal, and a fourth clock signal;
a phase difference between the first clock signal and the third clock signal provides an opposite phase pair;
a phase difference between the second clock signal and the fourth clock signal provides another opposite pair;
a phase difference between the first clock signal and the second clock signal is equal to 90 degrees;
a phase difference between the third clock signal and the fourth clock signal is equal to 90 degrees;
the first matched filter receives the first clock signal and the second clock signal;
the second matched filter receives the third clock signal the fourth clock signal; and
the clock optimization logic
(i) inputs the first clock signal to the first matched filter and the third clock signal to the second matched filter, and obtains two output values from the first matched filter and the second matched filter;
(ii) inputs the second clock signal to the first matched filter and the fourth clock signal to the second matched filter, and obtains two output values from the first matched filter and the second matched filter;
(iii) when the clock optimization logic specifies either one of the first matched filter and the second matched filter that outputs a greatest output value and a second greatest output value of the four output values obtained by the clock optimization logic,
the clock optimization logic inputs to the specified either one of the first matched filter and the second matched filter, a different clock signal that has a phase between two clock signals corresponding to the greatest value and the second greatest value to obtain a different output value,
the clock optimization logic specifies a maximum value of the obtained different output value regarding the either one of the first matched filter and the second matched filter, and
the clock optimization logic supplies the training sequence generator with the predetermined optimum operation clock signal based on the maximum value.
6. The waveform equalization apparatus according to claim 5, wherein:
when the training sequence generator receives the predetermined optimum operation clock signal, a tap coefficient of the waveform equalizer is converged.

1. An image registration method for use in medical imaging, the method including the steps of:
providing a sequence of images each one including a digital representation of a body-part under analysis,
selecting a reference image within the sequence, the remaining images of the sequence defining moving images, and
re-aligning at least one portion of a moving image with respect to the reference image,
wherein the step of re-aligning includes:

a) defining a delimitation mask identifying a region on the reference image with which the at least one portion of the moving image has to be re-aligned, and a feature mask identifying a further region on the reference image within which the re-alignment is calculated,
b) determining an optimized transformation for compensating a displacement of the moving image with respect to the reference image by optimizing a similarity measure between:
b1) a first computation region identified on the reference image by a computation mask and a second computation region identified by the computation mask on the moving image transformed according to a proposed transformation, the computation mask being determined by the intersection between the delimitation mask transformed according to the proposed transformation and the feature mask, or
b2) a first computation region identified by a computation mask on the reference image transformed according to a proposed transformation and a second computation region identified by the computation mask on the moving image, the computation mask being determined by the intersection between the feature mask transformed according to the proposed transformation and the delimitation mask, or
b3) a first computation region identified on the reference image by a computation mask transformed according to the reverse of a proposed transformation and a second computation region identified by the computation mask on the moving image, the computation mask being determined by the intersection between the feature mask transformed according to the proposed transformation and the delimitation mask, and

c) transforming the at least one portion of the moving image according to the optimized transformation.
2. The method according to claim 1, wherein the step c) of transforming includes:
c1) applying the optimized transformation to the at least one portion of the moving image when the optimized transformation is determined according to step b1), or
c2) applying the reverse of the optimized transformation to the at least one portion of the moving image when the optimized transformation is determined according to steps b2) or b3).
3. The method according to claim 1, wherein the step of determining the optimized transformation includes the iteration of calculating the similarity measure corresponding to the proposed transformation until the similarity measure or a change thereof reaches a threshold value.
4. The method according to claim 1, further including the iteration of the step of re-aligning for each further moving image of the sequence.
5. The method according to claim 1, wherein the feature mask is defined inside the delimitation mask.
6. The method according to claim 1, wherein the feature mask has a size larger than 50% of a size of the delimitation mask.
7. The method according to claim 1, wherein the similarity measure is a mutual information measure.
8. The method according to claim 1, further including the steps of:
estimating a spatial resolution along each dimension of the reference image,
calculating a sub-sampling factor for each dimension according to the spatial resolution, and
sub-sampling at least part of each image according to the sub-sampling factors.
9. The method according to claim 8, wherein the step of estimating the spatial resolution includes:
determining an estimation region on the reference image having a rectangular shape and being included in the smallest rectangle surrounding the feature mask, the spatial resolution being estimated in the estimation region.
10. An image registration method for use in medical imaging, the method comprising the steps of:
providing a sequence of images each one including a digital representation of a body-part under analysis,
selecting a reference image within the sequence, the remaining images of the sequence defining moving images, and
re-aligning at least one portion of a moving image with respect to the reference image,
wherein the step of re-aligning includes:

a) defining a delimitation mask identifying a region on the reference image with which the at least one portion of the moving image has to be re-aligned, and a feature mask identifying a further region on the reference image within which the re-alignment is calculated,
b) determining an optimized transformation for compensating a displacement of the moving image with respect to the reference image by optimizing a similarity measure between:
b1) a first computation region identified on the reference image by a computation mask and a second computation region identified by the computation mask on the moving image transformed according to a proposed transformation, the computation mask being determined by the intersection between the delimitation mask transformed according to the proposed transformation and the feature mask, or
b2) a first computation region identified by a computation mask on the reference image transformed according to a proposed transformation and a second computation region identified by the computation mask on the moving image, the computation mask being determined by the intersection between the feature mask transformed according to the proposed transformation and the delimitation mask, or
b3) a first computation region identified on the reference image by a computation mask transformed according to the reverse of a proposed transformation and a second computation region identified by the computation mask on the moving image, the computation mask being determined by the intersection between the feature mask transformed according to the proposed transformation and the delimitation mask, and

c) transforming the at least one portion of the moving image according to the optimized transformation; and
further including the iteration of the step of re-aligning for each further moving image of the sequence;
wherein the sequence includes at least one sub-sequence each one ordered from the reference image to a corresponding boundary image of the sequence, the step of determining the optimized transformation further including, for each next moving image being not adjacent to the reference image:
initializing the proposed transformation for the next moving image according to the optimized transformation for at least one previous moving image in the corresponding sub-sequence.
11. The method according to claim 10, wherein the at least one previous moving image consists of a plurality of previous moving images, the step of initializing the proposed transformation for the next moving image including:
estimating the proposed transformation for the next moving image through a predictive algorithm based on the optimized transformations for the previous moving images.
12. The method according to claim 10, further including the steps of:
skipping a number of moving images following a current moving image in the corresponding sub-sequence for defining the next moving image, said number being determined according to a gradient of the optimized transformation for the current moving image, and
interpolating the optimized transformation for each skipped moving image between the optimized transformation for the current moving image and the optimized transformation for the next moving image.
13. The method according to claim 10, wherein the reference image differs from the boundary images of the sequence, the at least one sub-sequence consisting of a sub-sequence ordered from the reference image to a first image of the sequence and a further sub-sequence ordered from the reference image to a last image of the sequence.
14. The method according to claim 1, wherein each transformation is a rigid transformation.
15. The method according to claim 1, wherein each image includes a plurality of visualizing elements each one representing a corresponding basic area of the body-part, for each moving image the method further including the step of:
discarding each visualizing element of the moving image or of the moving image transformed according to the optimized transformation being outside the delimitation mask.
16. The method according to claim 15, further including the steps of:
reducing each image by discarding each visualizing element being discarded in at least one of the moving images, and
determining a result of the analysis according to the reduced images.
17. The method according to claim 16, further including the steps of:
identifying a most intense image and a least intense image in the sequence,
for each basic area calculating a difference between the corresponding visualizing element in the most intense image and the corresponding visualizing element in the least intense image, and
discarding the visualizing elements for the basic area in all the images if the corresponding difference is lower than a threshold value.
18. The method according to claim 1, wherein each image includes a plurality of frames each one being representative of a corresponding slice of the body-part, the delimitation mask and the feature mask being defined on at least two of the frames of the reference image.
19. The method according to claim 1, wherein each image is representative of an ultrasound response of the body-part.
20. The method according to claim 1, wherein the analysis consists of the assessment of a perfusion of a contrast agent in the body-part.
21. A non-transitory computer readable medium carrying a computer program for performing the method of claim 1 when the computer program is executed on a data processing system.
22. A computer program product including a non-transitory computer-usable medium carrying a computer program, the computer program when executed on a data processing system causing the system to perform the method according to claim 1.
23. An image registration apparatus including means for performing the steps of the method according to claim 1.
24. A medical imaging system including the registration apparatus according to claim 23 and means for acquiring the sequence of images.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. An object detection apparatus which detects a specific object in an input image, the object detection apparatus comprising:
a specific object detecting module performing a specific object detection process of:
with a processor:
setting the input image or a reduced image of the input image as a target image, and generating an edge feature image of the target image,
determining whether the specific object exists in a determination region while scanning the determination region in the edge feature image of the target image,

wherein the specific object detecting module includes a determination module for determining whether the specific object exists in the determination region based on an edge feature amount of the edge feature image corresponding to the determination region and a previously determined relationship between an edge feature amount and a weight indicating object likelihood for each predetermined feature pixel in an image having the same size as the determination region,
wherein the specific object detecting module includes a specific object detecting table stored in a memory, the specific object detecting table previously prepared from a plurality of sample images, including the specific object, and storing a previously determined relationship between an edge feature amount and a weight indicating object likelihood for each predetermined feature pixel in the image having the same size as the determination region; and the determination module determining whether the specific object exists in the determination region based on the edge feature amount of the edge feature image corresponding to the determination region and the specific object detecting table,
wherein the specific object detection module prepares a plurality of kinds of determination regions having different sizes, the specific object detection module holding a plurality of specific object detecting tables according to a plurality of kinds of determination regions, the specific object detection module setting the plurality of kinds of the determination regions in the edge feature image of the target image, and the specific object detection module performing the specific object detecting process in each set determination region using a specific object detecting table corresponding to the determination region.
2. The object detection apparatus according to claim 1, wherein the specific object detection module prepares the plurality of kinds of the determination regions having the different sizes, the specific object detection module holds the plurality of specific object detecting tables according to the plurality of kinds of the determination regions and a specific object roughly-detecting table for detecting faces having all the sizes, the face being able to be detected by each determination region, the specific object detection module sets a common determination region including all the kinds of the determination regions in the edge feature image of the target image, the specific object detection means performs the specific object roughly-detecting process using the specific object roughly-detecting table, and the specific object detection module sets the plurality of kinds of the determination regions in the edge feature image of the target image and performs the specific object detecting process in each set determination region using the specific object detecting table corresponding to the determination region when a face is detected in the specific object roughly-detecting process.
3. The object detection apparatus according to claim 1, wherein the edge feature image is an edge feature image corresponding to each of the four directions of a horizontal direction, a vertical direction, an obliquely upper right direction, and an obliquely upper left direction, the feature pixel of the specific object detecting table is expressed by an edge number indicating an edge direction and an xy coordinate, a position in which the edge number of the feature pixel andor the xy coordinate is converted by a predetermined rule is used as a position on the edge feature image corresponding to any feature pixel of the specific object detecting table, and the specific object which is rotated by a predetermined angle with respect to a default rotation angle position of the specific object can be detected by the post-conversion position.
4. The object detection apparatus according to claim 1, wherein the edge feature image is an edge feature image corresponding to each of the four directions of a horizontal direction, a vertical direction, an obliquely upper right direction, and an obliquely upper left direction, the feature pixel of the specific object detecting table is expressed by an edge number indicating an edge direction and an xy coordinate, a position in which the edge number of the feature pixel andor the xy coordinate is converted by a predetermined rule is used as a position on the edge feature image corresponding to any feature pixel of the specific object detecting table, and the specific object in which a default attitude is horizontally flipped or the specific object in which a default attitude is vertically flipped can be detected by the post-conversion position.
5. An object detection apparatus which detects a specific object in an input image, the object detection apparatus comprising:
a reduced-image generating module, which with a processor, generates one or a plurality of reduced images from the input image; and
a specific object detection module, which with a processor, performs a specific object detecting process of setting each of a plurality of hierarchical images as a target image, and determines whether the specific object exists in a determination region while scanning the determination region in an edge feature image of the target image, the plurality of hierarchical images including the input image and one or a plurality of reduced images of the input image,
wherein the specific object detection module includes a determination module for determining whether the specific object exists in the determination region, based on an edge feature amount of the edge feature image corresponding to the determination region, and a previously determined relationship between an edge feature amount and a weight indicating object likelihood for each predetermined feature pixel in an image having the same size as the determination region,
wherein the specific object detection module prepares a plurality of kinds of the determination regions having different sizes, the specific object detection module storing in a memory a plurality of specific object detecting tables according to the plurality of kinds of the determination regions, the specific object detection module setting the plurality of kinds of the determination regions in the edge feature image of the target image, and the specific object detection module performing the specific object detecting process in each set determination region using the specific object detecting table corresponding to the determination region.
6. The object detection apparatus according to claim 5, wherein the specific object detection module prepares the determination region having the different size in each hierarchical target image, the specific object detection module holds the plurality of specific object detecting tables according to the determination regions, the specific object detection module performs a specific object roughly-detecting process to a lower hierarchical edge feature image of a lower hierarchical target image using the determination region corresponding to the lower hierarchy and the specific object detecting table corresponding to the determination region of the lower hierarchy when the specific object detection module performs the specific object detecting process to an arbitrary hierarchy, and the specific object detection module performs the specific object detecting process to the hierarchical edge feature image of the hierarchical target image using the determination region corresponding to the arbitrary hierarchy and the specific object detecting table corresponding to the determination region of the arbitrary hierarchy when a face is detected in the specific object roughly-detecting process.
7. The object detection apparatus according to claim 5, wherein the specific object detection module prepares a plurality of kinds of the determination regions having the different sizes, the specific object detection module holds the plurality of specific object detecting tables according to the plurality of kinds of the determination regions and a specific object roughly-detecting table for detecting faces having all the sizes, the face being able to be detected by each determination region, the specific object detection means sets a common determination region including all the kinds of the determination regions in the edge feature image of the target image, the specific object detection module performs the specific object roughly-detecting process using the specific object roughly-detecting table, and the specific object detection module sets the plurality of kinds of the determination regions in the edge feature image of the target image and performs the specific object detecting process in each set determination region using the specific object detecting table corresponding to the determination region when a face is detected in the specific object roughly-detecting process.
8. The object detection apparatus according to claim 5, wherein the edge feature image is an edge feature image corresponding to each of the four directions of a horizontal direction, a vertical direction, an obliquely upper right direction, and an obliquely upper left direction, the feature pixel of the specific object detecting table is expressed by an edge number indicating an edge direction and an xy coordinate, a position in which the edge number of the feature pixel andor the xy coordinate is converted by a predetermined rule is used as a position on the edge feature image corresponding to any feature pixel of the specific object detecting table, and the specific object which is rotated by a predetermined angle with respect to a default rotation angle position of the specific object can be detected by the post-conversion position.
9. The object detection apparatus according to claim 5, wherein the edge feature image is an edge feature image corresponding to each of the four directions of a horizontal direction, a vertical direction, an obliquely upper right direction, and an obliquely upper left direction, the feature pixel of the specific object detecting table is expressed by an edge number indicating an edge direction and an xy coordinate, a position in which the edge number of the feature pixel andor the xy coordinate is converted by a predetermined rule is used as a position on the edge feature image corresponding to any feature pixel of the specific object detecting table, and the specific object in which a default attitude is horizontally flipped or the specific object in which a default attitude is vertically flipped can be detected by the post-conversion position.